Opleidingen

Leer alles over schoonmaaktechnieken, reinigingsmiddelen, veiligheid, hygiëne en ergonomie zodat je voor ieder huishouden en uitstekende ondersteuning bent De thuishulpsector is booming! We hebben het allemaal druk en vinden steeds minder tijd om ons huishouden op orde te houden of om anderen hierin te helpen maar toch hechten we belang aan hygiëne en netheid. Een gouden kans voor professionals in de thuishulp om geld te verdienen! Wil jij ook een mooie en winstgevende carrière tegemoet gaan? Schrijf je dan in voor deze praktische thuisstudie en leer tegen de scherpste prijs hoe je als professional in de thuishulp mensen kunt helpen. Je leert alles over omgaan met hygiëne, mensen helpen met opstaan, schoonmaakproducten, voorkomen van schade aan meubilair, persoonlijke hygiëne en natuurlijk de privacyregels die je altijd moet respecteren bij jouw werkzaamheden. Ook de veiligheidsvoorschriften en tiltechnieken komen aan bod. Daarmee is deze opleiding ook zeer geschikt voor mantelzorgers die net een beetje extra willen geven. Na deze professionele opleiding ben jij op de hoogte van alle tools en technieken om professionele thuishulp aan te kunnen bieden. Vaardigheden: Jij kunt efficiënt en nauwkeurig iedere schoonmaakklus klaren Jij gebruikt de juiste middelen voor de juiste materialen Jij kunt mensen ondersteunen bij dagelijkse handelingen zoals zitten, opstaan en uit bed komen Je gaat professioneel om met opdrachtgevers Beroepsgeheim en privacy staan bij jou hoog in het vandeel Je bouwt een vlekkeloze naam uit als professional in de thuishulp Cursusinhoud: Organisatie van de thuiszorg Beroepsgeheim en privacy Persoonlijke hygiëne Hygiëne van de omgeving Reinigingstools en reinigingstechnieken Ergonomie Mensen helpen met opstaan en uit bed komen Tiltechnieken Onderhoud Soorten vlekken Productkennis Starten als zelfstandig ondernemer Je legt het examen af wanneer je er zelf klaar voor bent. Het theorie-examen is een online examen waarin je kennis over de lesstof wordt getoetst. Het online examen kan je volledig vanuit huis afleggen. Behaal je een score van minstens 60%, dan ben je geslaagd en ontvang je per post jouw diploma. Je kan direct aan de slag na je inschrijving! De digitale cursus staat meteen op jouw profielpagina. De papieren versie gaat onmiddellijk op de post en deze ontvang je snel thuis. Dankzij de flexibiliteit van een thuisstudie studeer je waar en wanneer je wil. Je krijgt een jaar lang begeleiding van een rasechte schoonmaakexpert via ons online leerplatform, dus je staat er niet alleen voor! Als je er klaar voor bent, leg je examen af op één van onze examenlocaties en als je Karijn Van Campen Toekomstmogelijkheden: Werk voor particulieren Ondersteunen van zorgbehoevenden als mantelzorger Je kunt deze opleiding volgen zonder vooropleiding. Ook voorkennis is niet nodig, we starten de lesstof vanaf de basis. Familiecoach, Eerste Hulp voor Kinderen, Timemanagement, Huishoudhulp

In de online DISC training leer je alles over DISC en vergroot je de kennis over jouw gedrag en dat van anderen. Hierdoor begrijp je elkaar veel beter! De online DISC training van een halve dag, via Zoom of Teams, is inclusief een Q4 Persoonlijke Stijl (DISC) Profiel. Wat is DISC? Het DISC model brengt iemands voorkeuren, kwaliteiten en valkuilen, in gedrag en communicatie in kaart. DISC bestaat uit 4 verschillende hoofdstijlen (D, I, S, en C) en is krachtig doordat het eenvoudig uit te leggen en te gebruiken is. Hieronder een overzicht van de basis kenmerken van de verschillende DISC stijlen: Dominant extravert en controlerend direct, gedreven, prestatiegericht, hoog tempo, neemt leiding. Interactief extravert en relaterend hartelijk, enthousiast, prater, impulsief, verbaal. Stabiel introvert en relaterend betrokken, vriendelijk, attent, teamplayer, harmonie, gesloten. Consciëntieus introvert en controlerend denker, observator, afwachtend, precies, gesloten, privacy. Let op dat we nooit maar 1 stijl hebben: niemand is alleen maar ‘rood’, ‘geel’, ‘groen’ of ‘blauw’, maar dat dit altijd een combinatie is. Deze combinatie, en intensiteit, van alle DISC stijlen bepaalt de voorkeuren die wij in gedrag en communicatie laten zien en wat wij van anderen, qua communicatie, nodig hebben. Inclusief DISC profiel Iedere deelnemer krijgt zijn eigen Q4 Persoonlijke Stijl DISC Profiel. Het Q4 Persoonlijke Stijl DISC Profiel is een gevalideerd instrument, goed en duidelijk leesbaar, en geoptimaliseerd naar de Nederlandse taal en culturele maatstaven. In de online DISC training komen op een interactieve en praktische manier de volgende onderwerpen aan bod: Introductie DISC, uitleg DISC model en de verschillende DISC communicatiestijlen. Effectief beïnvloeden van gedrag door af te stemmen op de DISC stijl van de ander. Zelfkennis vergroten, inzicht in eigen kwaliteiten en talenten. Wat maakt mij uniek en hoe kan ik mijn valkuilen vermijden? Toelichting op het eigen DISC profiel en de persoonlijke DISC grafiek. Duur: Een halve dag van 09:00 - 13:00. Voor wie is de online DISC training? De online DISC training is geschikt voor iedereen die meer inzicht wil in zijn of haar eigen DISC stijl en beter wil leren afstemmen op de gedrags- en communicatiestijl van de ander. Wie is de trainer? De online DISC training wordt gegeven door Patrick Schriel, gepokt en gemazeld in DISC. Waar vele trainers DISC erbij doen is het zijn specialiteit. Patrick heeft honderden DISC trainingen en workshops gegeven, werkt voor grote én kleine organisaties, in binnen- en buitenland, heeft duizenden DISC profielen door zijn handen laten gaan en is een echte DISC expert.

Dé online typecursus voor bedrijven en professionals! Human4active bedrijfszorg & coaching informeert onze cursisten over gezond werken. Een goede typevaardigheid is een mooie basis om makkelijker en efficiënter met een computer of laptop te kunnen werken. Het biedt u meer snelheid, meer flexibiliteit en een hogere accuratesse. Een goede typevaardigheid gegeven door een erkend type-instituut! Ook geven wij in-company trainingen bij bedrijven of op locatie. Voor meer info kunt u altijd contact met ons opnemen. Wat zijn de voordelen van een online typecursus voor Volwassenen bij Typ4work? Leren blindtypen in uw eigen tempo en 1 jaar lang toegang tot een digitale leeromgeving. Wekelijks voortgangsrapportage over uw persoonlijke vorderingen. Persoonlijk contact en een goede begeleiding van een ervaren vakdocent. Door een afname in de hoge werkdruk kunnen mensen meer ontspannen gaan werken. Werktempo gaat omhoog, productiviteit neemt toe. Gerichte aandacht op de inhoud van het werk in plaats van op het toetsenbord. Human4active bedrijfszorg & coaching informeert de cursisten over gezond werken. Typ4work (onderdeel Typ4fun) is een onderwijsinstelling en ingeschreven in het Centraal Register Kort Beroepsonderwijs (CRKBO) en is daarmee een erkende instelling. In de kwaliteitscode staat omschreven dat een CRKBO erkenning onder meer is gebaseerd op zorgvuldigheid, betrouwbaarheid, redelijkheid, kenbaarheid en rechtszekerheid. Deze online typecursus wordt begeleidt door een vakdocent die is aangesloten bij het Centraal Bureau Typevaardigheid (CBT) en is in het bezit van het certificaat Docent Typevaardigheid en daarmee bekwaam om de lessen typevaardigheid te verzorgen.

Dé online typecursus voor volwassenen! Human4active bedrijfszorg & coaching informeert onze cursisten over gezond werken. Een goede typevaardigheid is een mooie basis om makkelijker en efficiënter met een computer of laptop te kunnen werken. Het biedt u meer snelheid, meer flexibiliteit en een hogere accuratesse. Een goede typevaardigheid gegeven door een erkend type-instituut! Ook geven wij in-company trainingen bij bedrijven of op locatie. Voor meer info kunt u altijd contact met ons opnemen Wat zijn de voordelen van een online typecursus voor Volwassenen bij Typ4work? Leren blindtypen in uw eigen tempo en 1 jaar lang toegang tot een digitale leeromgeving. Wekelijks voortgangsrapportage over uw persoonlijke vorderingen. Persoonlijk contact en een goede begeleiding van een ervaren vakdocent. Door een afname in de hoge werkdruk kunnen mensen meer ontspannen gaan werken. Werktempo gaat omhoog, productiviteit neemt toe. Gerichte aandacht op de inhoud van het werk in plaats van op het toetsenbord. Human4active bedrijfszorg & coaching informeert de cursisten over gezond werken. Typ4work (onderdeel Typ4fun) is een onderwijsinstelling en ingeschreven in het Centraal Register Kort Beroepsonderwijs (CRKBO) en is daarmee een erkende instelling. In de kwaliteitscode staat omschreven dat een CRKBO erkenning onder meer is gebaseerd op zorgvuldigheid, betrouwbaarheid, redelijkheid, kenbaarheid en rechtszekerheid. Deze online typecursus wordt begeleidt door een vakdocent die is aangesloten bij het Centraal Bureau Typevaardigheid (CBT) en is in het bezit van het certificaat Docent Typevaardigheid en daarmee bekwaam om de lessen typevaardigheid te verzorgen.

Na inschrijving van de Award Winning E-learning training Defensive Programmer, ontvangt u per e-mail een link om in te loggen in uw leeromgeving waar u een persoonlijk wachtwoord aanmaakt. Eenmaal ingelogd in uw persoonlijke leeromgeving ziet u een overzicht van de cursusonderdelen. Via de inhoudsopgave schakelt u automatisch over naar elk gewenst onderdeel van de training. Er zit tevens een Voortgangsbewaking bij om eenvoudig te zien hoe ver u bent binnen uw training. U kunt daarbij 1 jaar lang (365 dagen), 24/7 (elke dag en nacht) inloggen om verder te gaan met de training. Deze Engelstalige training met ondertiteling heeft interactieve, eenvoudig te volgen video's in HD beeldkwaliteit met heldere audio kwaliteit. Daarnaast biedt de training Support en/of een Online Mentor aan als u problemen ondervindt. De training is beschikbaar in elke browser voor zowel PC, Mac, Tablet én Smartphone. Dus zelfs via uw mobiel kunt u handig de training volgen. De training is inclusief lees- en/of praktijkopdrachten met trainingstest, mits noodzakelijk voor de training. Na afronding van de training krijgt u een Certificaat van Deelname en sluit u zich aan bij de reeds duizenden tevreden cursisten. Defensief programmeren is een benadering van programmeren die probeert ervoor te zorgen dat software nog steeds functioneert onder ongunstige of onvoorziene omstandigheden. Dit leertraject behandelt hoe defensief coderen in Java. Het behandelt hoe beweringen en annotaties te gebruiken, hoe klassen en methoden en programmastroom veilig te implementeren, en hoe u denial of service-aanvallen en injectie-aanvallen kunt voorkomen. Ten slotte wordt ook besproken hoe u veilig met gegevens kunt omgaan, gelijktijdigheid effectief kunt beheren en toegangscontrole kunt gebruiken om veilige en effectieve toepassingen te bieden. Cursusinhoud Defensive Programmer: Defensive Concepts Course: 44 Minutes Course Overview General Defensive Coding Concepts CERT Top 10 Secure Coding Practices - Part A CERT Top 10 Secure Coding Practice - Part B Defensive Coding Open Source Security Testing Methodology Manual Flaw Hypothesis Method Six Sigma Course Summary Defensive Programmer: Defensive Techniques Course: 1 Hour, 17 Minutes Course Overview Exception Handling Validation Reliability, Resiliency, and Recoverability CDI/UDI Parameter Checking Java Exception Handling Code Example Python Exception Handling Code Example C# Exception Handling Code Example JavaScript Exception Handling Code Example Java Validation Code Example Python Validation Code Example C# Validation Code Example JavaScript Validation Code Example Trusting Software Components Intelligent Code Re-use Course Summary Defensive Programmer: Cryptography Course: 31 Minutes Course Overview Encryption Concepts - Part A Encryption Concepts - Part B Java Encryption Code Examples Python Encryption Code Examples C# Encryption Code Examples JavaScript Encryption Code Examples Course Summary Defensive Programmer: Advanced Concepts Course: 19 Minutes Course Overview Session Management Risk Management Assertive Programming Intelligible Exceptions Course Summary Defensive Programmer: Code Samples Course: 1 Hour, 35 Minutes Course Overview Java Filtering Code Example Python Filtering Code Example C# Filtering Code Example JavaScript Filtering Code Example Java Resilient Code Example Python Resilient Code Example C# Resilient Code Example JavaScript Resilient Code Example Java Recoverable Code Example Python Recoverable Code Example C# Recoverable Code Example JavaScript Recoverable Code Example Java Parameter Checking Code Example Python Parameter Checking Code Example C# Parameter Checking Code Example JavaScript Parameter Checking Code Example Java Validation Code Example Python Validation Code Example C# Validation Code Example JavaScript Validation Code Example Course Summary Defensive Programmer: Secure Testing Course: 29 Minutes Course Overview Secure Testing Concepts Secure Unit Testing Secure Regression Testing Secure Integration Testing Security Metrics Tracking Security Bugs Course Summary Specificaties Taal: Engels Kwalificaties van de Instructeur: Gecertificeerd Cursusformaat en Lengte: Lesvideo's met ondertiteling, interactieve elementen en opdrachten en testen Lesduur: 4:55 uur Voortgangsbewaking: Ja Toegang tot Materiaal: 365 dagen Technische Vereisten: Computer of mobiel apparaat, Stabiele internetverbindingen Webbrowserzoals Chrome, Firefox, Safari of Edge. Support of Ondersteuning: Helpdesk en online kennisbank 24/7 Certificering: Certificaat van deelname in PDF formaat Prijs en Kosten: Cursusprijs zonder extra kosten Annuleringsbeleid en Geld-Terug-Garantie: Wij beoordelen dit per situatie Award Winning E-learning: Ja Tip! Zorg voor een rustige leeromgeving, tijd en motivatie, audioapparatuur zoals een koptelefoon of luidsprekers voor audio, accountinformatie zoals inloggegevens voor toegang tot het e-learning platform.

De Masterclass Veel geleerd tijdens je opleiding? Met de kennis zit het wel goed! Maar je merkt dat de overgang van studie naar werk toch wel erg groot is. ​ Je voelt je onzeker over de gang van zaken in het bedrijf. Mag je na je inwerkperiode nog steeds vragen stellen? Hoe kun je je mening stevig onderbouwd neerzetten, zonder als betweter over te komen? Hoe ga je efficiënt met je tijd en energie om? Hoe kun je verder groeien en in welke richting dan?  ​Herken jij je in bovenstaande vragen, dan ben je niet alleen. ​Wat bieden wij Wij hebben een mooie Masterclass voor jou om je te helpen jouw talenten nog verder te ontwikkelen. Dit doen we in een kleine groep van maximaal 10 deelnemers gedurende twee dagdelen van 9 tot 12 uur. Aan bod komen je persoonlijke missie en visie met betrekking tot je loopbaan, hoe je het best communiceert met anderen afgestemd op jouw en hun persoonlijke stijl, én hoe je effectief met je tijd omgaat.  Voorafgaand aan deze online masterclass ontvang je van ons een vragenlijst voor de DISC gedragsstijlenanalsye. En plannen we een online intake voor deze training, om jouw DISC analyse te bespreken en jouw leerdoelen voor deze masterclass door te nemen. Wat is het resultaat Je staat sterker in je schoenen, kent jezelf nog beter, weet jezelf beter te positioneren en hebt jouw koers voor de komende tijd weer scherp op je netvlies.  Wat is de investering  De investering in deze masterclass bedraagt € 499,00 excl. btw, inclusief jouw persoonlijke DISC analyse en overige materialen. Je volgt de masterclass vanaf je eigen werkplek, met jouw eigen lekkere koffie of thee. De trainers De trainers zijn Katrien Eggens-ter Heide en Sonja Jaarsma-Lebbink. Wij hebben een ruime ervaring in het bedrijfsleven, in het geven van trainingen én met de doelgroep van jonge professionals. We worden gedreven door persoonlijke groei en zoeken altijd naar hoe het beter kan. Onze nuchtere noordelijke opvoeding en kijk houdt ons met beide benen op de grond.

Python / Data Science Python / Programmeren (algemeen) / Data Science with Python Masterclass.  Deze reis met meer dan 120 uur online content, zal eerst een basis bieden voor gegevensarchitectuur, statistieken en  programmeervaardigheden voor gegevensanalyse met behulp van Python en R, wat de eerste stap zal zijn in het verwerven van de kennis om over te stappen van het gebruik van ongelijksoortige en verouderde gegevensbronnen. Je leert dan om de data te wringen met Python en R en die data te integreren met Spark en Hadoop. Vervolgens leert u hoe u data kunt operationaliseren en schalen, rekening houdend met compliance en governance. Om de reis te voltooien, leert u vervolgens hoe u die gegevens neemt en visualiseert, om slimme zakelijke beslissingen te nemen. Dit leertraject, met meer dan 120 uur online content, is onderverdeeld in de volgende vier tracks: Data Science Track 1: Data Analyst Data Science Track 2: Data Wrangler Data Science Track 3: Data Ops Data Science Track 4: Data Scientist Data Science Track 1: Data Analyst In this track, the focus is the data analyst role with a focus on: Python, R, architecture, statistics, and Spark. Content: E-learning courses Data Architecture Primer Course: 1 Hour, 4 Minutes Course Overview Data Defined Data Privacy The Data Lifecycle SQL vs. NoSQL Create an Entity Relationship Diagram Implement a SQL Solution Implement a NoSQL Solution Big Data Data Architecture and Governance IT Data System Architecture Types Data Analytics and Reporting Exercise: Implement Data Architecture Best Practices Data Engineering Fundamentals Course: 46 Minutes Course Overview Overview of Distributed Systems Batch vs. In-Memory Processing NoSQL Stores Tools for Data Management What is ETL? ETL with Talend Open Studio Data Modeling AI and Machine Learning Data Partitioning Data Engineering Data Reporting Exercise: Create a Data Model Python for Data Science: Introduction to NumPy for Multi-dimentional Data Course: 1 Hour Course Overview Introduction to NumPy and the NumPy Ecosystem Array Creation - Part 1 Array Creation - Part 2 Printing Arrays Basic Array Operations Universal Functions Indexing and Slicing Iterating Over Arrays Reshaping Arrays Exercise: Python NumPy Array Operations Python for Data Science: Advanced Operations with NumPy Arrays Course: 1 Hour, 8 Minutes Course Overview Splitting NumPy Arrays Images as Arrays Image Manipulation Using NumPy Views and NumPy Arrays Deep Copies of Arrays Introduction to Index Masks Applying Index Masks Indexing with Boolean Masks Structured Arrays Understanding Array Broadcasting Applying Broadcasting Rules on Array Operations Exercise: NumPy Multi-dimensional Array Operations Python for Data Science: Introduction to Pandas Course: 1 Hour, 6 Minutes Course Overview Features of Pandas and the Pandas Ecosystem Introduction to Pandas Work with Pandas Introduction to DataFrames Work with DataFrames Load Data into a DataFrame Add and Delete DataFrame Contents Select Parts of a DataFrame Access Pandas DataFrames Introduction to Multi-Indexing in a Dataframe Reshape DataFrames Reshape Dataframes Using Stack and Melt Operations Exercise: Pandas for Basic Tabular Data Manipulation Python for Data Science: Manipulating and Analyzing Data in Pandas DataFrames Course: 45 Minutes Course Overview Iterating Over the Contents of a DataFrame Exporting a DataFrame Sorting Handling Missing Data Grouping with a Multi-Index Merging DataFrames Applying Join Operations on DataFrames Pandas and Relational Databases Exercise: Pandas for Advanced Data Manipulation R for Data Science: Data Structures Course: 52 Minutes Course Overview Creating Vectors Manipulating Vectors Sorting Vectors Using Lists Creating Matrices Matrix Operations Creating Factors Creating Data Frames Data Frame Operations Exercise: Creating and Using a Data Frame R for Data Science: Importing and Exporting Data Course: 34 Minutes Course Overview Reading from CSV Reading from Excel Reading from HTML Exporting to CSV Exporting to Excel Exporting to HTML Exercise: Reading and Writing Data R for Data Science: Data Exploration Course: 41 Minutes Course Overview Creating dplyr Tables Selecting Subsets Filtering Tabular Data Piping Data Mutating Data Summarizing Data Combining Datasets Grouping Data Exercise: Querying Data R for Data Science: Regression Methods Course: 37 Minutes Course Overview Linear Data Preparation Creating Linear Models Interpreting Model Output Using Linear Prediction Logistic Data Preparation Using glm Exercise: Creating a Linear Model R for Data Science: Classification & Clustering Course: 39 Minutes Course Overview Preparing Data for Classification Using rpart Using ctree Preparing Data for Clustering Using K-Means Clustering Using Hierarchical Clustering Exercise: Creating a Decision Tree Data Science Statistics: Simple Descriptive Statistics Course: 1 Hour, 11 Minutes Course Overview Descriptive and Inferential Statistics Population vs. Sample Probability vs. Non-Probability Sampling Mean Median Mode IQR Variance Exercise: Using Descriptive Statistics Data Science Statistics: Common Approaches to Sampling Data Course: 47 Minutes Course Overview Terms in Sampling Sampling Bias Simple Random Sampling Systematic Random Sampling Stratified Sampling Non-Probability Sampling Exercise: Efficient and Correct Sampling Data Science Statistics: Inferential Statistics Course: 1 Hour, 2 Minutes Course Overview Gaussian Distribution Inferential Statistics and Hypothesis Testing Simplified Example of Hypothesis Testing T-tests9 Skewness and Kurtosis Correlation and Autocorrelation Introducing Linear Regression Overfitting and Goodness-of-Fit Exercise: Basic Inferential Statistics Accessing Data with Spark: An Introduction to Spark Course: 1 Hour, 7 Minutes Course Overview Introduction to Spark and Hadoop Resilient Distributed Datasets (RDDs) RDD Operations Spark DataFrames Spark Architecture Spark Installation Working with RDDs Creating DataFrames from RDDs Contents of a DataFrame The SQLContext The map() Function of an RDD Accessing the Contents of a DataFrame DataFrames in Spark and Pandas Exercise: Working with Spark Getting Started with Hadoop: Fundamentals & MapReduce Course: 1 Hour, 4 Minutes Course Overview An Introduction to Big Data Building Systems to Scale with Data A Quick Overview of Hadoop MapReduce Overview The Map Phase of a MapReduce The Shuffle and Reduce Phases Exercise: Fundamentals of Hadoop and MapReduce Getting Started with Hadoop: Developing a Basic MapReduce Application Course: 1 Hour, 14 Minutes Course Overview Provisioning a Hadoop Cluster on the Cloud Browsing the Hadoop Web Applications Creating a MapReduce project Coding the Map Phase Coding the Reduce Phase Defining the Driver Program Building the Application Executing the MapReduce Application Exercise: Developing a Basic MapReduce Application Hadoop HDFS: Introduction Course: 1 Hour, 15 Minutes Course Overview Scaling Datasets Horizontal Scaling for Big Data Distributed Clusters and Horizontal Scaling Overview of HDFS HDFS Architectures MapReduce for HDFS YARN for HDFS The Mechanism of Resource Allocation in Hadoop Apache Zookeeper for HDFS The Hadoop Ecosystem Exercise: An Introduction to HDFS Hadoop HDFS: Introduction to the Shell Course: 53 Minutes Course Overview Creating a Hadoop Cluster on the Google Cloud Exploring Hadoop Clusters The YARN Cluster Manager UI The HDFS NameNode UI Browsing the Packaged Hadoop Tools Configuring HDFS The HDFS Shells Exercise: Introduction to the HDFS Shell Hadoop HDFS: Working with Files Course: 48 Minutes Course Overview Basic Directory Commands in HDFS Using the copyFromLocal Command in HDFS Using the put Command in HDFS Using the copyToLocal Command in HDFS Retrieving files from HDFS Append and Delete Operations in HDFS Exercise: Working with Files on HDFS Hadoop HDFS: File Permissions Course: 49 Minutes Course Overview The HDFS count and du Commands Viewing and Setting File Permissions in HDFS Applying Permissions Recursively in HDFS An Introduction to Bash Scripting Scripting HDFS Operations Exploring the HDFS NameNode UI Cleanup Operations in HDFS Exercise: File Permissions on HDFS Data Silos, Lakes, & Streams: Introduction Course: 1 Hour, 20 Minutes Course Overview Data Silos Data Lakes Characteristics of Data Lakes Data Lake Architecture, Features, and Challenges Data Warehouses Data Warehouses vs. Data Lakes Data Streams Migrating Data to AWS Data Lakes on AWS Working with Data Lakes on AWS Exercise: Data Silos, Lakes, and Streams Data Silos, Lakes, and Streams: Data Lakes on AWS Course: 1 Hour, 10 Minutes Course Overview Create a Role for the AWS Glue Service Upload Data to S Explore the Glue Web Console Manually Create Glue Tables Query the Data Lake Using Amazon Athena Configure and Run Glue Crawlers Access Data in Crawled Tables Crawl Multiple CSV Files in the Same Folder Path Merge Data in Multiple Files in the Same Folder Path Work with Files Having the Exact Same Schema Exercise: Data Lakes on AWS with S3 and Glue Data Silos, Lakes, & Streams: Sources, Visualizations, & ETL Operations Course: 1 Hour, 29 Minutes Course Overview Set Up a Redshift Cluster Create Tables and Load Data From S Establish a JDBC Connection to Redshift Crawl Redshift Using a JDBC Connection Crawl DynamoDB Configure QuickSight to Visualize Data Visualize Data in QuickSight Configure a Job to Perform Extract, Transform, Load Execute an ETL Operation in Glue Perform ETL to Back Up Redshift Data in S3 Buckets Perform ETL to Back Up DynamoDB Data in S3 Buckets Exercise: Multiple Sources, Visualizations, and ETL Data Analysis Application Course: 1 Hour, 25 Minutes Course Overview Install and Configure Anaconda Python Install R Using Anaconda Use Jupyter Notebook Import and Export Data in Python Import and Export Data in R Deal with Missing Data in R Transform Data in R Work with Numpy Work with Pandas Mean, Median, and Mode in R Analyze Data with Pandas Plot Data in R Visualize Data in Python Exercise: Perform Data Analysis Online Mentor You can reach your Mentor by entering chats or submitting an email. Final Exam assessment Estimated duration: 65 minutes Practice Labs: Analyzing Data with Python (estimated duration: 8 hours) Practice performing data analysis tasks using Python by configuring VSCode, loading data from SQLite into Pandas, grouping data and using box plots. Then, test your skills by answering assessment questions after using Python to calculate frequency distribution, measures of center, and coefficient of dispersion. This lab provides access to several tools commonly used in data science, including: VS Code, Anaconda, Jupyter Notebook + Hub, Pandas, NumPy, SiPy, Seaborn Library, Spyder IDE Data Science Track 2: Data Wrangler In this track, the focus will be on the data wrangler role. We will explore areas such as: wrangling with Python, Mongo, and Hadoop. Content: E-learning courses Data Wrangling with Pandas: Working with Series & DataFrames Course: 1 Hour, 11 Minutes Course Overview Installing Pandas Pandas Series Objects Operations on Series Appending and Sorting Series Values Pandas DataFrames Indexing Operations with DataFrames Missing Data Column Aggregations Statistical Operations Data Wrangling with Pandas: Visualizations and Time-Series Data Course: 1 Hour, 29 Minutes Course Overview Pandas and Matplotlib for Visualizations Pie Charts, Box Plots, and Scatter Plots Time-Series Data Deltas and Percentage Change Calculations Time Deltas and Date Ranges Mismatched DataFrames and Missing Data Working with String Data Advanced Operations on Strings Applying Functions on Series Transforming Data With User-Defined Functions Applying Functions on DataFrames Exercise: Plot Charts and Transform Column Values Data Wrangling with Pandas: Advanced Features Course: 1 Hour, 12 Minutes Course Overview Grouping and Aggregations MultiIndex DataFrames Grouping and Aggregations with MultiIndex DataFrames General Aggregation Functions Filtering Masking Column Values Working with Duplicates Working with Categorical Data Filtering, Adding, and Removing Categories Reindexing Exercise: Filtering, Duplicates and Categorical Data Data Wrangler 4: Cleaning Data in R Course: 1 Hour, 3 Minutes Course Overview Types of Unclean Data Data Quality Downloading JSON Data Excel Sheets Reading Dirty CSVs Querying Relational Databases Joining Tabular Data Spreading Data Summarizing Data Imputing Data Extracting Matches Exercise: Wrangling Data Data Tools: Technology Landscape & Tools for Data Management Course: 27 Minutes Course Overview Technology Landscape and Tools Tool Comparison Machine Learning in Data Analytics Machine Learning Tools Machine Learning Implementation Python and R for Data Management Cloud and Machine Learning Exercise: Implement Machine Learning on Scikit-learn Data Tools: Machine Learning & Deep Learning in the Cloud Course: 23 Minutes Course Overview Microsoft Machine Learning Toolkit AWS and Machine Learning Spark Machine Learning Capabilities Deep Learning Frameworks Deep Learning Implementation Data Mining and Analytical Tools KNIME Capabilities Exercise: Implement Deep Learning Trifacta for Data Wrangling: Wrangling Data Course: 50 Minutes Course Overview Standardizing Data Formatting Dates Filtering Rows Replacing Values Counting Matches Splitting Columns Merging Columns Extracting Data Conditional Aggregation Reshaping Data Joining Data Exercise: Wrangling Data MongoDB for Data Wrangling: Querying Course: 1 Hour, 8 Minutes Course Overview Introduction to PyMongo Document Structure CRUD Operations ObjectID and Timestamp Query Operations Projection Queries Comparison Operators Element Query Operators The Regex Operator Using the Size and All Operators Text Search Using mongoimport Using mongoexport Exercise: Performing a Query MongoDB for Data Wrangling: Aggregation Course: 51 Minutes Course Overview Aggregation Framework Using Group Using Match Using Project Using Limit and Sort Using Unwind Using Lookup Using Indexes Using Geospatial Indexes Exercise: Performing an Aggregate Query Getting Started with Hive: Introduction Course: 56 Minutes Course Overview Hive as a Data Warehouse Overview of Relational Databases OLTP and OLAP Hive and the Hadoop Ecosystem HiveServer and The Metastore Hive on Cloud Computing Platforms Data Types in Hive Data and Tables in Hive Exercise: Introduction to Hive Getting Started with Hive: Loading and Querying Data Course: 1 Hour, 20 Minutes Course Overview Setting up a Hadoop Cluster on the Google Cloud Creating a Hive Table Running Simple Queries in Hive Executing Hive Queries from the Shell Joining Tables in Hive Exploring the Hive Warehouse External Tables in Hive Modifying Tables in Hive Temporary Tables in Hive Loading Data into Tables in Hive Populating Multiple Tables in Hive Exercise: Loading and Querying Data in Hive Getting Started with Hive: Viewing and Querying Complex Data Course: 1 Hour, 14 Minutes Course Overview The Array Data Type in Hive The Map Data Type in Hive The Struct Type in Hive The explode and posexplode Functions in Hive Lateral Views in Hive Multiple Lateral Views in Hive Set Operations in Hive The IN and EXISTS clauses in Hive Creating and Populating Tables in Hive Views in Hive Exercise: Viewing and Querying Complex Data Getting Started with Hive: Optimizing Query Executions Course: 43 Minutes Course Overview Hive Queries as MapReduce Jobs Techniques to Improve Query Performance in Hive Partitioning Tables in Hive Bucketing Tables in Hive Structuring Join Queries in Hive Exercise: Optimizing Query Execution in Hive Getting Started with Hive: Optimizing Query Executions with Partitioning Course: 1 Hour, 1 Minute Course Overview Setting up a Hadoop Cluster on the Google Cloud Creating a Partitioned Table in Hive Working with Partitions in Hive Populating Partitions in Hive Partitioning External Tables in Hive Modifying Partitions in Hive Dynamic Partitions in Hive Using Multiple Columns for Partitioning in Hive Exercise: Optimize Executions with Partitioning Getting Started with Hive: Bucketing & Window Functions Course: 1 Hour, 4 Minutes Course Overview Apply Bucketing for a Table in Hive Using Bucketing and Partitioning Together in Hive Sorting a Bucket's Contents in Hive Sampling a Table in Hive Joining Multiple Tables in Hive Introducing Window Functions in Hive Windows Functions with Partitions in Hive Exercise: Bucketing and Window Functions in Hive Getting Started with Hadoop: Filtering Data Using MapReduce Course: 59 Minutes Course Overview Counting the Data Points in Each Category The Reducer and Driver Programs Building and Executing the Application A Simple Filter Using MapReduce Executing and Examining the Output Extracting the Unique Values in a Column Viewing the Distinct Values Extracted Exercise: Filtering Data Using MapReduce Getting Started with Hadoop: MapReduce Applications With Combiners Course: 1 Hour, 24 Minutes Course Overview Combiners in MapReduce Revisiting MapReduce Working with Combiners Using Combiners for Calculating Averages Creating a Project to Calculate Averages Coding the Map and Reduce Phases8 Configure the Application in the Driver Executing the Application and Examining the Output Adding a Combiner to a MapReduce Application Conveying a Pair of Numbers from the Mapper Running the Fixed Application Exercise: Optimizing MapReduce With Combiners Getting Started with Hadoop: Advanced Operations Using MapReduce Course: 49 Minutes Course Overview Defining a User-Defined Type for a PriorityQueue Implementing a PriorityQueue in a Mapper Using a PriorityQueue in a Reducer Running and Verifying the Results Building an Inverted Index - Map Phase Building an Inverted Index - Reduce Phase Executing the Application and Viewing the Index Exercise: Advanced Operations Using MapReduce Accessing Data with Spark: Data Analysis Using the Spark DataFrame API Course: 1 Hour, 12 Minutes Course Overview Performance Improvements in Spark Broadcast Variables and Accumulators Loading Data into a DataFrame Sampling the Contents of a DataFrame Grouping and Aggregations Visualizing Data in a DataFrame Trimming and Cleaning Data User-Defined Functions and DataFrames Combining Filters, Aggregations, and Sorting Using Broadcast Variables Using Accumulators Exporting DataFrame Contents Custom Accumulators Join Operations Exercise: Data Analysis Using the DataFrame API Accessing Data with Spark: Data Analysis using Spark SQL Course: 55 Minutes Course Overview The Spark Catalyst Optimizer Introduction to Spark SQL Preparing Data for Analysis Running SQL Queries Inferred and Explicit Schemas Windowing in Spark Applying Window Functions Exercise: Data Analysis Using Spark SQL Data Lake: Framework & Design Implementation Course: 34 Minutes Course Overview Data Lakes and Data Warehouses Data Lake Selection Criteria Data Lake and Data Democratization Data Lake Design Principles AWS Data Lake Architecture Implement AWS Data Store Data Lake For On-Premise and Multi-Cloud Data Processing Frameworks for Data Lake Exercise: Implement AWS Data Store Data Lake: Architectures & Data Management Principles Course: 35 Minutes Course Overview Real-Time Big Data Architectures Data Lake Reference Architecture Data Ingestion and File Formats Ingestion Using Sqoop Data Processing Strategies Deriving Value from Data Lakes Data Life Cycle S3 and Glacier Exercise: Ingest Data and Implement Archival Policy Data Architecture - Deep Dive: Design & Implementation Course: 36 Minutes Course Overview Data Complexity Management Strategies Data Modeling Process Distributed Data Management Partitioning Methods and Criteria MongoDB Partitioning Hybrid Data Architectures Implement Directed Acyclic Graph CAP Theorem Batch vs. Streaming Read and Write Concerns Exercise: Implement Serverless Architecture Data Architecture - Deep Dive: Microservices & Serverless Computing Course: 26 Minutes Course Overview Microservices and Data Serverless and Lambda Architecture Lambda Implementation Cluster Benefits Data Architecture Types Data Discovery Process Data Risk Types Data POC Exercise: Implement Lambda Architecture Online Mentor You can reach your Mentor by entering chats or submitting an email. Final Exam assessment Estimated duration: 90 minutes Nova Learning, januar 2021 Practice Labs: Data Wrangling with Python (estimated duration: 8 hours) Perform data wrangling tasks including using a Pandas DataFrame to convert multiple Excel sheets to separate JSON documents, extract a table from an HTML file, use mean substitution and convert dates within a DataFrame. Then, test your skills by answering assessment questions after using a Pandas DataFrame to convert a CSV document to a JSON document, replace missing values with a default value, split a column with a delimiter and combine two columns by concatenating text. Data Science Track 3: Data Ops The tracks objective is to help prepare the learner for a Data Ops role with a focus on governance, security, and harnessing volume and velocity. Content: E-learning courses Deploying Data Tools: Data Science Tools Course: 48 Minutes Course Overview Data Science Platform Challenges of Deploying Data Science Tools Considerations for Data Science Tools Data Science Workflow Data Science Analytic Tools Data Science Visualization Tools Data Science Database Tools Benefits of Deploying Cloud-Based Tools Challenges of Deploying Cloud-Based Tools What is DevOps DevOps for Data Science Exercise: Identifying Uses of Data Science Tools Delivering Dashboards: Management Patterns Course: 34 Minutes Course Overview Analytical Visualization Dashboard Types Data Management Dashboard Components Dashboard Best Practices Dashboard Using ELK Dashboard Using Power BI Chart Selection Criteria Leaderboards and Scorecards Scorecard Types Exercise: Create Dashboards with PowerBI and ELK Delivering Dashboards: Exploration & Analytics Course: 31 Minutes Course Overview Data Exploration Using Charts Analytical Visualization Tools Bar and Line Charts Dashboarding with Kibana Dashboard Sharing with Kibana Dashboarding with Tableau Dashboarding with Qlikview Data Ingest and Dashboards Dashboard Patterns Monitoring Dashboards Exercise: Create Dashboards Using Kibana and Tableau Cloud Data Architecture: DevOps & Containerization Course: 45 Minutes Course Overview Containerization on the Cloud Benefits of Containers Serverless Computing DevOps in the Cloud AWS OpsWorks Storage Classification Cloud and Machine Learning Cloud and BI Analytics Exercise: Containerization and Serverless Computing Compliance Issues and Strategies: Data Compliance Course: 44 Minutes Course Overview Data Compliance Issues Data Regulations The Importance of Global Standards Risk and Company Standards Myths and Facts of Data Compliance Compliance Training for Users Compliance Training for Management The Benefits of a Data Compliance Program Elements of a Good Compliance Strategy Building a Compliance Strategy Reporting and Response Procedures Exercise: Explain the Importance of Data Compliance Implementing Governance Strategies Course: 46 Minutes Course Overview Governance and its Relationship with Big Data Why Big Data Requires Governance Requirements for Big Data Governance Why is Big Data Different? Identifying Data Identifying Stakeholders Cloud Technologies and Data Governance Designing a Data Governance Process Managing a Data Governance Strategy Monitoring a Data Governance Strategy Maintaining a Data Governance Strategy Exercise: Defining Data Governance Strategies Data Access & Governance Policies: Data Access Oversight and IAM Course: 59 Minutes Course Overview Data Access Governance Risk and Data Safety Compliance Data Access Patterns Data Breach Prevention Least Privilege Assign and View Effective File System Permissions Identity and Access Management Create an AWS IAM User and Group Assign AWS IAM Group Permissions Vulnerability Assessments Implement Effective Security Controls Exercise: Implement Data Access Governance Solutions Data Access & Governance Policies: Data Classification, Encryption, and Monitoring Course: 1 Hour, 19 Minutes Course Overview Data Classification Classify Data Using Microsoft FSRM Data Encryption Encrypt Data at Rest Encrypt Data in Motion Implement Security Compliance Checking Examine Data Access Trends Data Access Monitoring Solutions Logging, Auditing, and Data Analytics Configure a Custom Filtered Log View Enable Windows Data Access Auditing Exercise: Implement Data Confidentiality Streaming Data Architectures: An Introduction to Streaming Data Course: 51 Minutes Course Overview Introduction to Streaming data The Stream Processing Model The Message Transport Stream Processing with RDDs Structured Streaming for Continuous Applications Streaming vs Structured Streaming Triggers and Output Modes Exercise: Working with Streaming Data Streaming Data Architectures: Processing Streaming Data Course: 53 Minutes Course Overview PySpark Setup Setting Up a Socket Stream with Netcat The Update Output Mode Using a File Input Stream The Append Output Mode The Complete Output Mode Aggregations on Streaming Data SQL Operations on Streaming Data User-Defined Functions (UDFs) Exercise: Processing Streaming Data Scalable Data Architectures: Introduction Course: 53 Minutes Course Overview Scalable Architectures with Distributed Computing Introducing Data Warehouses Contrasting Warehouses with Relational Databases Data Warehouses for Analytical Processing Data Warehouse Architectural Components Amazon Redshift - A Data Warehouse on the Cloud Exercise: Scalable Data Architectures Scalable Data Architectures: Introduction to Amazon Redshift Course: 55 Minutes Course Overview Provisioning a Redshift Cluster Using Quick Launch Creating a Redshift Cluster With Additional Detail Exploring the Redshift Configs and Metrics Attaching an IAM Role to a Redshift Cluster Creating an AWS User to Work With Redshift Installing and Configuring the AWS CLI Running Queries from the Redshift Query Editor Exercise: An Introduction to Amazon Redshift Scalable Data Architectures: Working with Amazon Redshift & QuickSight Course: 1 Hour, 18 Minutes Course Overview Loading Data from Amazon S3 to a Redshift Cluster Running Queries and Evaluating Their Execution Querying a Redshift Cluster Using a SQL client Working with Automated Snapshots Restoring Tables from a Snapshot Horizontal Scaling of a Redshift Cluster Vertical and Horizontal Scaling of a Cluster Configuring Access from QuickSight to Redshift Loading a Dataset to QuickSight Creating Visualizations with QuickSight Exercise: Working with Redshift and QuickSight Building Data Pipelines Course: 1 Hour, 10 Minutes Course Overview Data Pipelines Overview Traditional ETL Pipeline with Batch Processing Data Pipeline Tools Setup and Install Airflow Apache Airflow Airflow Workflows Airflow Tasks Airflow Dependencies ETL Pipeline with Airflow Automated Pipeline without ETL Airflow Command Line Testing Exercise: Using Apache Airflow Data Pipeline: Process Implementation Using Tableau & AWS Course: 39 Minutes Course Overview Data Pipeline Data Pipeline Processes Data Pipeline Stages Data Pipeline Technologies Data Source Types Scheduled Data Pipeline Tableau Server and Utilities Data Pipeline Using Tableau Data Pipeline on AWS Exercise: Build Data Pipelines with Tableau Data Pipeline: Using Frameworks for Advanced Data Management Course: 33 Minutes Course Overview Celery and Luigi Data Pipeline with Python Luigi Working with Dask Library Dask Arrays Data Exploration and Visualization Frameworks Spark and Tableau Streaming Data Visualization with Python Data Pipeline Open Source Tools Exercise: Implement Data Pipelines with Luigi Data Sources: Integration Course: 40 Minutes Course Overview Elements of IoT Solutions Service Categories in IoT IoT Capabilities and Maturity Model IoT Design Principles IoT Cloud Architectures MQTT and XXMP IoT Controllers IoT Data Management Securing IoT Exercise: Generating Data Streams Data Sources: Implementing Edge on the Cloud Course: 31 Minutes Course Overview AWS IoT Greengrass GCP IoT Edge AWS IoT over WebSockets IoT Device Simulator Generating Streams of Data Using MQTT Exercise: Working with IoT Device Simulators Securing Big Data Streams Course: 1 Hour, 3 Minutes Course Overview Big Data Security Concerns Streaming Data Security Concerns NoSQL Database Security Concerns Distributed Processing Security Risks Data Mining and Analytics Privacy Flaws End-Point Device Tampering Risks Secure Big Data Secure Data Streams Secure Data In Motion End-Point Input Validation and Filtering Secure Data at Rest with Symmetric Ciphers Exercise: Securing Big Data Streams Harnessing Data Volume & Velocity: Big Data to Smart Data Course: 39 Minutes Course Overview Comparing Big Data and Smart Data Smart Data and Edge Technologies Big Data to Smart Data Formation Smart Data and Smart Processes Smart Data Use Cases Smart Data Life Cycle Big Data to Smart Data Using k-NN Smart Data Frameworks Smart Data to Business Clustering Smart Data Smart Data Integration Exercise: Transform Big Data to Smart Data Data Rollbacks: Transaction Rollbacks & Their Impact Course: 36 Minutes Course Overview Rollback Process State of Transactions Transaction Types SQL Transaction Management Transaction Log Operations Deadlock Management SQL Server Rollback Mechanism SQL Server Rollback Mechanism Implementation Exercise: Implement Transactions with SQL Server Data Rollbacks: Transaction Management & Rollbacks in NoSQL Course: 29 Minutes Course Overview NoSQL and SQL Transaction Management MongoDB Transactions Manage Multi-Document Transactions in MongoDB Change Data Capture Change Stream in MongoDB MongoDB Change Stream Implementation Exercise: MongoDB Transactions and Change Streams Online Mentor You can reach your Mentor by entering chats or submitting an email. Final Exam assessment Estimated duration: 90 minutes Practice Labs: Implementing Data Ops with Python (estimated duration: 8 hours) Perform data ops tasks with Python including working with row subsets, creating new columns with Regex, performing joins and spreading rows. Then, test your skills by answering assessment questions after working with field subsets and computed columns, and performing set operations and binding rows. Data Science Track 4: Data Scientist For this track, the focus will be on the Data Scientist role. Here we will explore areas such as: visualization, APIs, and ML and DL algorithms. Content: E-learning courses Balancing the Four Vs of Data: The Four Vs of Data Course: 40 Minutes Course Overview Overview of the Four Vs The Importance of Volume The Importance of Variety The Importance of Velocity The Importance of Veracity The Relationship Between the Four Vs Variety and Data Structure Validity and Volatility Finding Balance in the Four Vs Use Cases Extracting Value from the Four Vs Exercise: Describe the Four Vs of Big Data Data Driven Organizations Course: 1 Hour, 15 Minutes Course Overview Data Driven Organizations Decision Making Analytic Maturity Analytic Roles Data Source Priority Facets of Data Quality Power BI Data Visualization Missing Data Duplicate Data Truncated Data Data Provenance Raw Data to Insights: Data Ingestion & Statistical Analysis Course: 54 Minutes Course Overview Statistical Analysis Data Correction Outlier Detection Data Architecture Pattern Data Ingestion Tools Kafka and Apache NiFi Apache Sqoop Ingest Ingest Using WaveFront Raw Data to Insights: Data Management & Decision Making Course: 57 Minutes Course Overview Data-driven Decision Making Framework Loading Data into R Preparing Data Data Correction Approach Data Correction Using Simple Transformation Data Correction Using Deductive Correction Distributed Data Management Data Analytics Data Analytics Using R Predictive Modeling Tableau Desktop: Real Time Dashboards Course: 1 Hour, 8 Minutes Course Overview Introducing Real Time Dashboards Creating Real Time Dashboards with Tableau Build a Tableau Dashboard Real Time Dashboard Updates in Tableau Organizing Your Tableau Dashboard Formatting Your Tableau Dashboard Interactive Tableau Dashboard Tableau Dashboard Starters Tableau Dashboard Extensions Tableau Dashboards and Story Points Sharing your Tableau Dashboard Storytelling with Data: Introduction Course: 47 Minutes Course Overview Storytelling Process Interpreting Context Analysis Types Who, What, and How of Storytelling Visualization for Storytelling Graphical Tools for Data Elaboration Storytelling Scenarios Storyboarding Storytelling with Data: Tableau & PowerBI Course: 57 Minutes Course Overview Visual Selection Slopegraphs Bar Charts and Types of Bar Charts Clutter and Clutter Elimination Gestalt Principle Story Design Best Practices Tools for Storytelling Decluttering Crafting Visual Data Visual Design Concerns Storytelling with Power BI Model Visual and Tableau Python for Data Science: Basic Data Visualization Using Seaborn Course: 1 Hour, 7 Minutes Course Overview Introduction to Seaborn Install Seaborn Simple Univariate Distributions Configure Univariate Distribution Plots Simple Bivariate Distributions Explore Different Types of Bivariate Distributions Analyze Multiple Variable Pairs Regression Plots Themes and Styles in Seaborn Python for Data Science: Advanced Data Visualization Using Seaborn Course: 1 Hour, 4 Minutes Course Overview Searching for Patterns in a Dataset Configuring Plot Aesthetics Normal Distribution and Outliers Distributions Within Categories - Part Distributions Within Categories - Part Analyzing Categories with Facet Grids - Part Analyzing Categories with Facet Grids - Part Introducing Color Palettes Using Color Palettes Data Science Statistics: Using Python to Compute & Visualize Statistics Course: 1 Hour, 16 Minutes Course Overview An Introduction to Matplotlib Analyzing Data Using NumPy and Pandas Visualizing Univariate and Bivariate Distributions Summary Statistics Using Native Python Functions Summary Statistics Using NumPy Summary Statistics Using the SciPy Library Correlation and Covariance Z-score R for Data Science: Data Visualization Course: 33 Minutes Course Overview An Introduction to Matplotlib Analyzing Data Using NumPy and Pandas Visualizing Univariate and Bivariate Distributions Summary Statistics Using Native Python Functions Summary Statistics Using NumPy Summary Statistics Using the SciPy Library Correlation and Covariance Z-score Advanced Visualizations & Dashboards: Visualization Using Python Course: 38 Minutes Course Overview Relevance of Data Visualization for Business Libraries for Data Visualization in Python Python Data Visualization Environment Configuration Matplotlib Libraries for Visualization Bar Chart Using ggplot Bokeh and Pygal Select Visualization Libraries Interactive Graphs and Image Files Plot Graphs Multiple Lines in Graphs Advanced Visualizations & Dashboards: Visualization Using R Course: 35 Minutes Course Overview Chart Types Stacked Bar Plot Animate Plots with Matplotlib Plotting in Jupyter Notebook Graphics in R Heat Map and Scatter Plot in R Correlogram and Area Chart in R ggplot2 Capabilities Customize ggplot2 Graphs Powering Recommendation Engines: Recommendation Engines Course: 1 Hour, 5 Minutes Course Overview Describing Recommendation Engines Comparing the Types of Recommendation Engines Collecting and Manipulating Data Manipulating Data in R Describing Similarity and Neighborhoods Creating a Recommendation Engine Recommending Another Item Finding Items to Recommend Recommending Items Based on Other Items Evaluating a Recommendation System Validating a Recommendation System Data Insights, Anomalies, & Verification: Handling Anomalies Course: 46 Minutes Course Overview Data and Anomaly Sources Decomposition and Forecasting Examine Data Using Randomization Tests Anomaly Detection Anomaly Detection Techniques Anomaly Detection with scikit-learn Anomaly Detection Tools Anomaly Detection Rules Data Insights, Anomalies, & Verification: Machine Learning & Visualization Tools Course: 51 Minutes Course Overview Machine Learning Anomaly Detection Techniques Comparing Anomaly Detection Algorithms Anomaly Detection Using R Online Anomaly Detection Components Online Anomaly Detection Approaches Anomaly Detection Use Cases Anomaly Detection with Visualization Tools Anomaly Detection with Mathematical Approaches Cluster-Based Anomaly Detection Data Science Statistics: Applied Inferential Statistics Course: 1 Hour, 19 Minutes Course Overview The One-Sample T-test Independent and Paired T-tests Testing Hypotheses with T-tests Loading and Analyzing a Skewed Dataset Measuring Skewness and Kurtosis Preparing a Dataset for Regression Simple Linear Regression Multiple Linear Regression Data Research Techniques Course: 33 Minutes Course Overview Data Research Fundamentals Data Research Steps Values, Variables, and Observations JMP Scale of Measurement Non-experimental and Experimental Research Descriptive and Inferential Statistical Analysis Inferential Tests Case Study of Clinical Data Research Data Research in Sales Management Data Research Exploration Techniques Course: 50 Minutes Course Overview Fundamentals of Exploratory Data Analysis Data Exploration Types Working with R Data Exploration in R Data Exploration Using Plots Python Packages for Data Exploration Data Exploration Using Python Data Research Using Linear Algebra Linear Algebra for Data Research Data Research Statistical Approaches Course: 43 Minutes Course Overview Role of Statistics in Data Research Discrete vs. Continuous Distribution PDF and CDF Binomial Distribution Interval Estimation Point and Interval Estimation Data Visualization Techniques Data Visualization Using R Data Integration Techniques Creating Plots Missing Values and Outliers Machine & Deep Learning Algorithms: Introduction Course: 46 Minutes Course Overview Machine Learning Algorithms How Machine Learning Works Introduction to Pandas ML Support Vector Machines Overfitting Machine & Deep Learning Algorithms: Regression & Clustering Course: 49 Minutes Course Overview The Confusion Matrix An Introduction to Regression Applications of Regression Supervised and Unsupervised Learning Clustering Principal Component Analysis Machine & Deep Learning Algorithms: Data Preperation in Pandas ML Course: 1 Hour, 4 Minutes Course Overview Data Preparation in scikit-learn Training and Evaluating Models in scikit-learn Introducing the Pandas ML ModelFrame Training and Evaluating Models in Pandas ML Preparing Data for Regression Evaluating Regression Models Preparing Data for Clustering The K-Means Clustering Algorithm Machine & Deep Learning Algorithms: Imbalanced Datasets Using Pandas ML Course: 1 Hour, 24 Minutes Course Overview Analyzing an Imbalanced Dataset The RandomOverSampler The SMOTE Oversampler Undersampling Using imbalanced-learn Ensemble Classifiers for Imbalanced Data Combination Samplers Finding Correlations in a Dataset Building a Multi-Label Classification Model Dimensionality Reduction with PCA Imbalanced Learn and PCA Creating Data APIs Using Node.js Course: 1 Hour, 31 Minutes Course Overview API Prerequisites Building a RESTful API Using Node.js and Express.js RESTful API with OAuth HTTP Server with Hapi.js API Modules Returning Data with JSON Nodemon for Development Workflow API Requests POSTman for API Deploying APIs Social Media APIs Exercise: Building RESTful APIs Online Mentor You can reach your Mentor by entering chats or submitting an email. Final Exam assessment Estimated duration: 90 minutes Practice Labs: Data Visualization with Python (estimated duration: 8 hours) Perform data visualization tasks with Python such as creating scatter plots, plotting linear regression, using logistic regression and creating decision tree. Then, test your skills by answering assessment questions after creating time-series graphs, resampling observations, creating histograms and using a grid pair. Specificaties Taal:Engels Kwalificaties van de Instructeur: Gecertificeerd Cursusformaat en Lengte: Lesvideo's met ondertiteling, interactieve elementen en opdrachten en testen Lesduur: 120 uur Assesments: De assessment test uw kennis en toepassingsvaardigheden van de onderwerpen uit het leertraject. Deze is 365 dagen beschikbaar na activering. Online mentor: U heeft 24/7 toegang tot een online mentor voor al uw specifieke technische vragen over het studieonderwerp. De online mentor is 365 dagen beschikbaar na activering, afhankelijk van de gekozen Learning Kit. Online Virtuele labs: Ontvang 12 maanden toegang tot virtuele labs die overeenkomen met de traditionele cursusconfiguratie. Actief voor 365 dagen na activering, beschikbaarheid varieert per Training. Voortgangsbewaking: Ja Toegang tot Materiaal: 365 dagen Technische Vereisten: Computer of mobiel apparaat, Stabiele internetverbindingen Webbrowserzoals Chrome, Firefox, Safari of Edge. Support of Ondersteuning: Helpdesk en online kennisbank 24/7 Certificering: Certificaat van deelname in PDF formaat Prijs en Kosten: Cursusprijs zonder extra kosten Annuleringsbeleid en Geld-Terug-Garantie: Wij beoordelen dit per situatie Award Winning E-learning: Ja Tip! Zorg voor een rustige leeromgeving, tijd en motivatie, audioapparatuur zoals een koptelefoon of luidsprekers voor audio, accountinformatie zoals inloggegevens voor toegang tot het e-learning platform.

Blockchain Masterclass.  Met Blockchain kunt u uw organisatie onderscheiden van de concurrentie met veilige distributie voor supply chain management, medische dossiers, productie, detailhandel en meer. Als zodanig zijn Blockchain Solutions Architects van vitaal belang voor bedrijven die zaken doen met de buitenwereld. Verken de verschillende fasen die nodig zijn om een ​​Blockchain Solutions Architect te worden. BLOCKCHAIN - APP DEVELOPER TO BLOCKCHAIN SOLUTIONS ARCHITECT Dit leertraject, met meer dan 78 uur online content, is opgedeeld in de volgende vier tracks: Track 1: Blockchain Application Developer Track 2: Blockchain Smart Contracts Programmer Track 3: Blockchain Engineer Track 4: Blockchain Solutions Architect Cursusinhoud Track 1: Blockchain Application Developer In this track of the Blockchain journey, the focus is getting started with Blockchain and discovering Ethereum. Content: E-learning courses Blockchains & Ethereum: Introduction Course: 59 Minutes Course Overview An Overview of Blockchains An Introduction to Ethereum Mining and Ether Chaining of Blocks A Blockchain as a Distributed Ledger Transactions Miners and Gas Exercise: Introducing Blockchains and Ethereum Blockchains & Ethereum: Performing Transactions in Ethereum Course: 1 Hour, 8 Minutes Course Overview Cryptographic Hashing In Blockchains Chains Of Blocks Merkle Trees Transaction Verification Digital Signatures and Identity Verifying Sender's Balance Transaction Nonce Exercise: Implementing Transactions in Ethereum Blockchains & Ethereum: Mining and Smart Contracts in Ethereum Course: 1 Hour, 15 Minutes Course Overview Transaction Ordering and Consensus Consensus by Proof of Work Finding the Proof of Work Nonce Claiming the Mining Rewards Beyond Proof of Work Smart Contracts The Solidity Programming Language Gas - the Measure of Complexity of Transactions Exercise: Mining and Smart Contracts Working with Ethereum: Storing Data Course: 59 Minutes Course Overview Revisiting Blockchain Concepts A Glossary of Ethereum Terms States in Ethereum The Trie Data Structure The Merkle Patricia Trie - Part The Merkle Patricia Trie - Part Exercise: Storing Data in Ethereum Working with Ethereum: Smart Contract Development Course: 1 Hour, 14 Minutes Course Overview Ethereum Test Network The Ethereum Virtual Machine Compiling Smart Contracts Ethereum Nodes Ethereum Clients Metamask Developing Smart Contracts The Truffle Suite Exercise: Smart Contract Development8 Working with Ethereum: Metamask & the Ethereum Wallet Course: 1 Hour, 27 Minutes Course Overview Installing Metamask Loading Ether Using the Rinkeby Faucet Installing the Ethereum Wallet Application Transferring Ether Using Metamask Deploying a Smart Contract with Ethereum Wallet Interacting with a Deployed Smart Contract Constructors in Solidity Constructors with Arguments in Solidity Defining a Token Transfer Function Writing to a Smart Contract Conditions in a Solidity Contract Cleaning Up Ethereum Wallet7 Exercise: Metamask and the Ethereum Wallet Working with Ethereum: The Geth Client Course: 46 Minutes Course Overview Setting Up a Private Blockchain With Geth The Geth Console Mining Blocks Using Geth Invoking Transactions with Geth Adding a New Node to a Network Exercise: The Geth Ethereum Client Working with Ethereum: Lifecycle of a Smart Contract Course: 46 Minutes Course Overview Developing a Contract with Solidity Compiling a Solidity Smart Contract Writing a Compile Script The Mocha Test Framework Integrating Mocha with Ethereum Exercise: The Lifecycle of a Smart Contract Working with Ethereum: Tools for Smart Contract Development Course: 1 Hour, 17 Minutes Course Overview Writing a Contract Deployment Script Testing a Function on a Deployed Contract Separating Deployment from Testing The Ganache Framework Interacting with a Contract from JavaScript Deploying a Contract to Rinkeby The Remix IDE Invoking Transactions Using Remix and Metamask Programmatic Interaction with the Rinkeby Network Accessing a Deployed Contract from Ethereum Wallet Exercise: Tools for Smart Contract Development Online Mentor You can reach your Mentor by entering chats or submitting an email. Final Exam assessment Estimated duration: 90 minutes Practice Labs: Blockchain Application Developer (estimated duration: 8 hours) Practice Blockchain Application Developer tasks such as working with cryptographic functions, developing, compiling, and deploying a smart contract in Remix. You will also set up a private Ethereum network. Then, test your skills by answering assessment questions after interacting with a deployed smart contract. This lab provides access to tools typically used by Blockchain Application Developers, including: Ubuntu 16.04 LTS, NPM/Node, Git, Ethereum, Geth, Chrome, VS Code Track 2: Blockchain Smart Contracts Programmer In this Skillsoft Aspire track of the Blockchain journey, the focus is coding smart contracts with Solidity and building smart contracts with Hyperledger. Content: E-learning courses Ethereum Smart Contracts with Solidity: An Overview of Ethereum and Solidity Course: 57 Minutes Course Overview Fundamentals of Blockchain Characteristics of Ethereum Smart Contracts An Overview of Solidity Solidity Bytecode and Opcode Pragmas and Comments Exercise: Overview of Ethereum and Solidity Ethereum Smart Contracts with Solidity: Features of the Solidity Language Course: 1 Hour, 22 Minutes Course Overview Primitive Types in Solidity Payable Functions and Function Modifiers Complex Types in Solidity Reference Types in Solidity Units and Global Variables Function Visibility Control Structures, Events and Inheritance Ether Transfer Functions Building Smart Contracts with solc and Remix The Truffle Suite Exercise: Features of Solidity Ethereum Smart Contracts with Solidity: The Remix Solidity IDE Course: 1 Hour, 21 Minutes Course Overview Primitive Types in Solidity Payable Functions and Function Modifiers Complex Types in Solidity Reference Types in Solidity Units and Global Variables Function Visibility Control Structures, Events and Inheritance Ether Transfer Functions Building Smart Contracts with solc and Remix The Truffle Suite Exercise: Features of Solidity Ethereum Smart Contracts with Solidity: Functions in Solidity Course: 1 Hour, 23 Minutes Course Overview Deploying Contracts With Constructor Arguments Introducing Functions View Functions Functions Modifying the Contract State Defining the Return Types of a Function Pure Functions Function Modifiers Function Polymorphism Contract Inheritance Abstract Contracts Defining Visibility Levels Testing the Effects of Different Visibility Levels Exercise: Functions in Solidity Ethereum Smart Contracts with Solidity: Ether Transfer Operations in Solidity Course: 1 Hour, 27 Minutes Course Overview Fallback Functions Defining an Ether Transfer Function Invoking an Ether Transfer Extending Fallback Functions Limitations of Fallback Functions The selfdestruct Function Introducing Arrays The require and assert Clauses Provisioning Variables in Memory and Storage Value and Reference Types Setting Provisioning Location for Reference Types Exercise: Ether Transfer Operations in Solidity Ethereum Smart Contracts with Solidity: Data & Control Structures in Solidity Course: 1 Hour, 11 Minutes Course Overview Building a Voting App with Solidity Interacting with the Voting App Block and Transaction Properties Introducing the Mapping Data Structure Enhancing the Voting App Using Structs with Composite Data Complex Data in Mapping Instances Control Structures: If Statements and For Loops Control Structures: While and Do while Loops Exercise: Data and Control Structures in Solidity Ethereum Smart Contracts with Solidity: Build Decentralized Apps Course: 1 Hour, 18 Minutes Course Overview Enhancing the Voting App Smart Contract Interacting with the Voting App Events Creating a Bank with a Smart Contract - Part Creating a Bank with a Smart Contract - Part Creating a Bank with a Smart Contract - Part Creating a Bank with a Smart Contract - Part Setting up an Escrow with a Smart Contract - Part Setting up an Escrow with a Smart Contract - Part Setting up an Escrow with a Smart Contract - Part Exercise: Building Decentralized Apps with Solidity Smart Contracts & Hyperledger Fabric: Foundations of Hyperledger Fabric Course: 1 Hour, 29 Minutes Course Overview An Overview of Blockchains Public Blockchain Implementations The Goals of the Hyperledger Project Hyperledger Frameworks Setting up a Fabric Network from Scratch A Tech-Based Approach to Building a Fabric Network Introducing Hyperledger Composer Network Definition Files in Hyperledger Composer Hyperledger Composer Playground Deploying a Fabric Network Definition Exercise: Fundamentals of Hyperledger Fabric Smart Contracts & Hyperledger Fabric: Setting Up a Hyperledger Fabric Network Course: 1 Hour, 19 Minutes Course Overview Application Portability Containers and Docker Running Docker Containers Fabric Prerequisite Installation - Docker and Go Installation Files for Hyperledger Fabric Exploring the Downloaded Fabric Artifacts The configtx.yaml File The crypto-config.yaml File The Connection Profile Exercise: Setting Up Hyperledger Fabric Smart Contracts & Hyperledger Fabric: Working with Fabric Chaincode in Golang Course: 1 Hour, 7 Minutes Course Overview Generating the Genesis Block and Channel The docker-compose File - Part The docker-compose File - Part Provisioning the Fabric Network Writing Chaincode in Go - Part Writing Chaincode in Go - Part Deploying Chaincode Exercise: Fabric Chaincode in Golang Smart Contracts & Hyperledger Fabric: Working with Fabric Chaincode in NodeJS Course: 43 Minutes Course Overview Fabric Network Setup Chaincode in Node.js - Init and Invoke Chaincode in Node.js - Add and Retrieve Chaincode in Node.js - the package.json File Chaincode Deployment Exercise: Fabric Chaincode in Node.js Smart Contracts & Hyperledger Fabric: Hyperledger Fabric Web App Course: 1 Hour Course Overview Creating an Admin User for a Fabric Network Creating an Application User for a Fabric Network Querying Deployed Chaincode from a Node App Fabric Web App - Building the Backend Fabric Web App - Building the UI Testing the Fabric App Exercise: Building a Fabric Web App Smart Contracts & Hyperledger Fabric: Hyperledger Composer Playground Course: 1 Hour, 32 Minutes Course Overview Hyperledger Composer Playground on the Cloud The Transaction Processing Script The ACL File Deploying the Blockchain Network Testing the Blockchain Network Building a Business Blockchain Network from Scratch Defining Resources and Transactions Creating Identities and Instances of Resources Testing Permissions and Transactions - Part 1 Testing Permissions and Transactions - Part 2 Exporting the Network Definition Exercise: Hyperledger Composer Playground Smart Contracts & Hyperledger Fabric: Web Apps for Hyperledger Composer Networks Course: 1 Hour, 13 Minutes Course Overview Installing the Pre-requisites Provisioning a Hyperledger Fabric Network Defining and Installing the Network Definition The Composer REST Server Testing the REST API calls Invoking Transactions from the REST Server Setting up the Hyperledger Composer Angular App Testing the Composer Angular App Exercise: Web Apps for Hyperledger Composer Online Mentor You can reach your Mentor by entering chats or submitting an email. Final Exam assessment Estimated duration: 90 minutes Practice Labs: Blockchain Smart Contracts Programmer (estimated duration: 8 hours) Practice Smart Contracts Programmer tasks such as creating smart contracts in Remix, adding function to a smart contract, and testing, reviewing, and extending a smart contract. Then, test your skills by answering assessment questions after creating a Hyperledger fabric chain and creating queries fabric chaincode. This lab provides access to tools typically used by Smart Contracts Programmer. Track 3: Blockchain Engineer In this track of the Blockchain journey, the focus is the Hyperledge Fabric and Working with the Truffle Suite. Content: E-learning collections Truffle Suite: Introduction Course: 1 Hour, 34 Minutes Course Overview An Overview of Ethereum Ethereum Smart Contracts Contrasting Traditional and Smart Contracts Building and Running Smart Contracts Simplifying Smart Contract Development An Overview of the Truffle Suite The Need for Ganache Features of Ganache Compiling Truffle Project Smart Contracts Testing & Deploying Truffle Project Smart Contracts Drizzle Exercise: Introduction to the Truffle Suite Truffle Suite: BlockBuilding Private Blockchain Networks with Ganache Course: 1 Hour, 8 Minutes Course Overview Developing a Smart Contract Using Solidity Deploying and Interacting with a Smart Contract Testing the Features of a Smart Contract Installing Ganache Connecting to Ganache Deploying a Smart Contract to a Ganache Network Viewing Transaction Data in Ganache Exercise: Private Blockchain Networks with Ganache Truffle Suite: Automating Development with the Truffle Framework Course: 1 Hour, 1 Minute Course Overview Initializing a Truffle Project Exploring a Truffle Project Building Smart Contracts in a Truffle Project Writing a Test for a Truffle Project Executing Truffle Tests Grouping Test Cases into Suites Deploying Contracts in a Truffle Project Exercise: The Truffle Framework Truffle Suite: Using Drizzle to Build Decentralized Apps Course: 1 Hour, 30 Minutes Course Overview Initializing a React Application Importing a Smart Contract into a React Application Defining Contract Interactions Configuring MetaMask to use Ganache Network Testing the React Application Initializing a Drizzle Application Coding the Top-Level Component of the Drizzle App Invoking Functions from a Drizzle App Invoking Transactions from a Drizzle App Testing the Drizzle Application Exercise: Building dApps with Drizzle Blockchain & Hyperledger Fabric: An Overview of Blockchain Technology Course: 1 Hour, 23 Minutes Course Overview An Overview of Blockchains Blockchain Integrity Centralized and Decentralized Ledgers A Ledger Use Case: Supply Chain Management Blockchains as a Distributed Ledger Cryptographic Hashing in Blockchains Chains of Blocks The Need for Smart Contracts Characteristics of Smart Contracts Exercise: An Introduction to Blockchains Blockchain & Hyperledger Fabric: An Overview of Hyperledger Course: 52 Minutes Course Overview Blockchain Implementations The Limitations of Ethereum The Origins of Hyperledger Hyperledger Frameworks Hyperledger Tools Exercise: The Hyperledger Project Blockchain & Hyperledger Fabric: The Hyperledger Fabric Course: 1 Hour, 5 Minutes Course Overview Blockchains for Enterprises Introducing Hyperledger Fabric Transaction Flow in Fabric: Execution Transaction Flow in Fabric: Order and Validate Channels in Fabric Identities in Fabric Exercise: Describing the Hyperledger Fabric Online Mentor You can reach your Mentor by entering chats or submitting an email. Final Exam assessment Estimated duration: 90 minutes Practice Labs: Blockchain Engineer (estimated duration: 8 hours) Practice blockchain engineering tasks by creating and working with a smart contract using Truffle, Ganache, Drizzle and React.js. Then, test your skills by answering assessment questions. This lab provides access to tools typically used by Blockchain Engineers. Track 4: Blockchain Solutions Architect In this track of the Blockchain journey, the focus is building decentralized applications and building blockchains on the cloud. Content: E-learning collections Building Decentralized Applications for Ethereum: An Introduction to dApps Course: 58 Minutes Course Overview The Case for Blockchains Recording Transactions with Blockchains An Introduction to Decentralized Applications The Truffle Suite Ethereum Tokens The ERC-20 Token Standard Exercise: Introduction to Ethereum dApps Building Decentralized Applications for Ethereum: Building the Back End Course: 1 Hour, 24 Minutes Course Overview Creating a Private Ethereum Network with Ganache Setting up the Metamask Plugin Installing the Truffle Framework Developing a Crowdfunding Contract - Part Developing a Crowdfunding Contract - Part Defining the Deployment Process Writing a Test Script for a Smart Contract Running the Tests for a Smart Contract Deploying Smart Contracts to an Ethereum Network Exercise: Building dApp Back Ends Building Decentralized Applications for Ethereum: Building the Front End Course: 1 Hour, 29 Minutes Course Overview Installing React and Drizzle Building the React App - The Root Component Building the React App - Retrieving Contract Data Building the React App - Contract Interactions Starting the Web Application Invoke Transactions from the Web Application Testing the dApp - the Success Scenario Redeploying the Contract Testing the dApp - the Failure Scenario Exercise: Building dApp UIs Building Decentralized Applications for Ethereum: Bespoke Ethereum Tokens Course: 49 Minutes Course Overview Event Definitions for an ERC20 Token Function Definitions for an ERC20 Token Deploying the Token Contract Testing Token Operations - Part Testing Token Operations - Part Exercise: Defining Ethereum Tokens Cloud Blockchains: An Introduction to Blockchain on the Cloud Course: 1 Hour, 12 Minutes Course Overview The Case for Blockchains Blockchain Solutions An Enterprise-Grade Blockchain Solution Blockchains on the Cloud Azure Blockchain Workbench - Part Azure Blockchain Workbench - Part Amazon Managed Blockchain Exercise: Introduction to Blockchain on the Cloud Cloud Blockchains: Single Organization Networks on Amazon Managed Blockchain Course: 1 Hour, 40 Minutes Course Overview Setting up the Client User Setting up a Security Group Setting up an EC2 Instance Creating a Network with AWS Managed Blockchain Connecting to the Client Installing the Prerequisites for the Fabric Client Configuring the Fabric CA Client Fabric Chaincode - Part Fabric Chaincode - Part Provisioning the Peer Node Provisioning the Channel Deploying and Interacting with Chaincode Exercise: Single Organization Networks Cloud Blockchains: Multi-Organization Networks on Amazon Managed Blockchain Course: 1 Hour, 23 Minutes Course Overview User and Security Group for Organization Two Inviting a Member to the Blockchain Network Accepting an Invitation to a Blockchain Network Configuring the Client for the Second Organization Setting up Fabric CA for the Second Organization Transferring Certificates between Organizations Creating a Multi-Organization Fabric Channel Instantiating Chaincode on the New Channel Testing the Multi-Organization Fabric Channel Exercise: Multi-Organization Blockchain Networks Cloud Blockchains: Building Apps on the Azure Blockchain Workbench Course: 1 Hour, 31 Minutes Course Overview Deploying Azure Blockchain Workbench Setting up Members for the Blockchain Network Setting up the Azure Blockchain Workbench UI Developing a Solidity Smart Contract Building the Application Configuration File Deploying the Blockchain Application Instantiating a Contract Contract Interaction - Requesting the Asset Adding a Member to a Deployed Application Contract Interaction - Terminating the Asset Exercise: Azure Blockchain Workbench Online Mentor You can reach your Mentor by entering chats or submitting an email. Final Exam assessment Estimated duration: 90 minutes Practice Labs: Blockchain Solutions Architect (estimated duration: 8 hours) Practice Blockchain Solutions Architect tasks such as designing and implementing an Ethereum Smart Contract. Then, test your skills by answering assessment questions after creating a token on the Ethereum network. This lab provides access to tools typically used by Blockchain Solutions Architects. Specificaties Taal:Engels Kwalificaties van de Instructeur: Gecertificeerd Cursusformaat en Lengte: Lesvideo's met ondertiteling, interactieve elementen en opdrachten en testen Lesduur: 78 uur Assesments: De assessment test uw kennis en toepassingsvaardigheden van de onderwerpen uit het leertraject. Deze is 365 dagen beschikbaar na activering. Online mentor: U heeft 24/7 toegang tot een online mentor voor al uw specifieke technische vragen over het studieonderwerp. De online mentor is 365 dagen beschikbaar na activering, afhankelijk van de gekozen Learning Kit. Online Virtuele labs: Ontvang 12 maanden toegang tot virtuele labs die overeenkomen met de traditionele cursusconfiguratie. Actief voor 365 dagen na activering, beschikbaarheid varieert per Training. Voortgangsbewaking: Ja Toegang tot Materiaal: 365 dagen Technische Vereisten: Computer of mobiel apparaat, Stabiele internetverbindingen Webbrowserzoals Chrome, Firefox, Safari of Edge. Support of Ondersteuning: Helpdesk en online kennisbank 24/7 Certificering: Certificaat van deelname in PDF formaat Prijs en Kosten: Cursusprijs zonder extra kosten Annuleringsbeleid en Geld-Terug-Garantie: Wij beoordelen dit per situatie Award Winning E-learning: Ja Tip! Zorg voor een rustige leeromgeving, tijd en motivatie, audioapparatuur zoals een koptelefoon of luidsprekers voor audio, accountinformatie zoals inloggegevens voor toegang tot het e-learning platform.

Machine Learning Masterclass.  Machine Learning Architects interpreteren realtime analyse van gegevens om de efficiëntie in alle bedrijfsdomeinen te automatiseren en te verhogen, en zo de weg vrij te maken voor zinvolle AI die van reactief naar voorspellend gaat. Deze reis zal je begeleiden in de overgang van een ML-programmeur naar een ML / DL-architectmeester via mechanismen zoals computertheorie. This learning path, with more than 100 hours of online content, is divided into the following four tracks: ML Track 1: ML Programmer ML Track 2: DL Programmer ML Track 3: ML Engineer ML Track 4: ML Architect Track 1: Machine Learning Programmer In this track of the machine learning journey, the focus is linear regression, computational theory, and training sets. Content: E-learning courses NLP for ML with Python: NLP Using Python & NLTK Course: 1 Hour, 3 Minutes Course Overview Uses and Challenges of NLP Terminologies and Steps of NLP Parsing Approach and Parser Types Corpus and Corpus Linguistic Regular Expressions in Python NLP Libraries NLTK Setup Components of NLP Tokenization Tokenization with NLTK Stop Words with NLTK Exercise: NLP Terminologies and Stopworks NLP for ML with Python: Advanced NLP Using spaCy & Scikit-learn Course: 41 Minutes Course Overview Stemming and Lemmatization Synonyms and Antonyms with NLTK Topic Extraction with LDA NER and Standard Libraries POS Tagging and NLTK Implementations spaCy Framework Analyzing and Processing Texts Text Classification Using scikit-learn Sentiment Analysis Exercise: Sentiment Analysis with scikit-learn Linear Algebra and Probability: Fundamentals of Linear Algebra Course: 1 Hour, 41 Minutes Course Overview Linear Algebra and Machine Learning Class of Spaces Types of Vector Space Linear Product Vector and Theorems Vector Arithmetic Vector Scalar Multiplication Vector Norms Matrix Arithmetic Working with Matrix Matrix Operations Matrix Decomposition Exercise: Vector Norms and Matrix Arithmetic Linear Algebra & Probability: Advanced Linear Algebra Course: 1 Hour, 44 Minutes Course Overview Matrix and PCA Sparse Matrix Tensor Arithmetic Hadamard Product and Tensors Singular-Value Decomposition Reconstruct Rectangular Matrix Using SVD Probability Probability Basics and Propositions Random Variable Central Limit Theorem Parameter Estimation and Gaussian Distribution Binomial Distribution Exercise: Tensor Arithmetic and Hadamard Product Linear Regression Models: Introduction to Linear Regression Course: 1 Hour, 19 Minutes Course Overview Statistical Tools and Regression Reasons to Use Regression Regression Loss: Least Square Error Capturing Variance in Regression Prediction Using Regression Introduction to Deep Learning The Architecture of Neural Networks Neurons: The Building Blocks of a Neural Network Linear Regression Using a Single Neuron Training a Neural Network Gradient Descent Optimization Exercise: Introduction to Linear Regression Linear Regression Models: Building Simple Regression Models with Scikit Learn and Keras Course: 42 Minutes Course Overview Statistical Tools and Regression Reasons to Use Regression Regression Loss: Least Square Error Capturing Variance in Regression Prediction Using Regression Introduction to Deep Learning The Architecture of Neural Networks Neurons: The Building Blocks of a Neural Network Linear Regression Using a Single Neuron Training a Neural Network Gradient Descent Optimization Exercise: Introduction to Linear Regression Linear Regression Models: Multiple and Parsimonious Linear Regression Course: 1 Hour, 11 Minutes Course Overview Understanding Multiple Regression Kitchen Sink Regression Training and Evaluating the Model Preparing Data for a Neural Network Building a Neural Network Evaluating the Neural Network Finding Correlations in a Dataset Introducing Parsimonious Regression Applying Parsimonious Regression with Scikit Learn Exercise: Multiple Linear Regression Linear Regression Models: An Introduction to Logistic Regression Course: 58 Minutes Course Overview Introducing Logistic Regression The Logistic Regression Curve Logistic Regression and Classification Logistic Regression vs. Linear Regression Logistic Regression in Keras Preparing Data for Logistic Regression Classification using a Logistic Regression Model Preparing Data for a Neural Network Building and Evaluating the Keras Classifier Exercise: An Introduction to Logistic Regression Linear Regression Models: Simplifying Regression and Classification with Estimators Course: 36 Minutes Course Overview Introducing Estimators Preparing Data for a Linear Regressor Estimator Training and Evaluating a Regressor Estimator Preparing Data for a Linear Classifier Estimator Training and Evaluating a Classifier Estimator Exercise: Using TensorFlow Estimators Computational Theory: Language Principle & Finite Automata Theory Course: 45 Minutes Course Overview Theory of Computation Computation Models Automata Theory and Classes Principles of Finite State Machine Principles of Formal Languages and Automata Theory Elements of Formal Language Regular Expressions Regular Grammar Closure Properties of Regular Languages Context-Free Grammar Features Exercise: Computation Theory and Formal Language Computational Theory: Using Turing, Transducers, & Complexity Classes Course: 47 Minutes Course Overview Analytical Capabilities of Grammar Normal Forms in Context-Free Grammar Pushdown Automata Turing Machines Turing Machine Themes Finite Transducers Types Computation Limitations Computational Complexity P and NP Class Recursively Enumerable Languages Exercise: Turing Machines and Finite Transducers Model Management: Building Machine Learning Models & Pipelines Course: 32 Minutes Course Overview Machine Learning Algorithms and Models Machine Learning Model Types Machine Learning Model Development Creating and Saving ML Models with scikit-learn Models for Regression and Classification Management Building Machine Learning Pipelines Machine Learning Pipeline Tools Machine Learning Pipeline Implementation Iterative Machine Learning Model Exercise: Build Machine Learning Models & Pipelines Model Management: Building & Deploying Machine Learning Models in Production Course: 56 Minutes Course Overview Hyperparameter Tuning Hyperparameter Tuning with Grid Search Reproducing Study Machine Learning Metrics Machine Learning Model Versioning Machine Learning Model Versioning with Git and DVC ModelDB Architecture Model Management Framework Studio.ml Setup Machine Learning Model Creation Machine Learning Model in Production Deploying Machine Learning Model in Production Exercise: Hyperparameter Tuning and Model Versioning Bayesian Methods: Bayesian Concepts & Core Components Course: 1 Hour, 1 Minute Course Overview Bayesian Probability and Statistical Inference Bayes' Theorem in Machine Learning Frequentist and Subjective Probability Probability Distribution Ingredients of Bayesian Statistics Bayesian Methods Bayesian Concepts in ML Modeling Prior Knowledge Distribution Bayesian Analysis Approach Exercise: Bayesian Statistics and Analysis Bayesian Methods: Implementing Bayesian Model and Computation with PyMC Course: 48 Minutes Course Overview Bayesian Learning Bayesian Model Types Probabilistic Programming Modeling with PyMC Bayesian Data Analysis Process Bayesian Data Analysis with PyMC Bayesian Computation Methods Markov Chain Simulation Implementing Markov Chain Simulation Finding Posterior Modes Exercise: Bayesian Modeling with PyMC Bayesian Methods: Advanced Bayesian Computation Model Course: 52 Minutes Course Overview Bayesian Model and Linear Regression Hierarchical Linear Model Probability Model Building Probability Models Non-Linear Model Gaussian Process Mixture Model Dirichlet Process Model Bayesian Modeling with PyMC Exercise: Implement Bayesian models Reinforcement Learning: Essentials Course: 30 Minutes Course Overview Reinforcement Learning Basics Reinforcement Learning and Machine Learning Reinforcement Learning Flow State Change and Transition Process Rewards and Reinforcement Learning Agents in Reinforcement Learning Types of Reinforcement Learning Environment OpenAI Exercise: Reinforcement Learning Elements Reinforcement Learning: Tools & Frameworks Course: 35 Minutes Course Overview Reinforcement Learning Types Reinforcement Learning with Keras and Python Markov Decision Process Q-Learning Concepts TensorFlow Installation Reinforcement Learning and TensorFlow Q-learning and Python Exercise: Reinforcement Learning with Python Math for Data Science & Machine Learning Course: 1 Hour, 2 Minutes Course Overview Work with Vectors Basis and Projection of Vectors Work with Matrices Matrix Multiplication Matrix Division Linear Transformations Gaussian Elimination Determinants Orthogonal Matrices Eigenvalues Eigenvectors Pseudo Inverse Exercise: Math for Data Science and Machine Learning Building ML Training Sets: Introduction Course: 1 Hour, 10 Minutes Course Overview Loading and Exploring a Dataset The Binarizer The MinMaxScaler The StandardScaler The Normalizer The MaxAbsScaler Label Encoding One-Hot Encoding Exercise: Building ML Training Sets Building ML Training Sets: Preprocessing Datasets for Linear Regression Course: 51 Minutes Course Overview Loading and Analyzing a Dataset Scaling and Encoding the Data Analyzing the Effects of Preprocessing Standardizing Continuous Data Exercise: Preprocessing Data for Regression Building ML Training Sets: Preprocessing Datasets for Classification Course: 44 Minutes Course Overview Loading and Scaling a Dataset Spotting Correlations in a Dataset Principal Component Analysis Normalizing a Dataset Exercise: Processing Data for Classification Linear Models & Gradient Descent: Managing Linear Models Course: 48 Minutes Course Overview Linear Model and its Classification Linear Modeling Approach Generalized Linear Model ANOVA and ANCOVA Linear Model Implementation Bias, Variance and Regularization Ensemble Techniques Bagging Implementation Implementing Boosting Algorithm Exercise: Linear Models and Ensemble Linear Models & Gradient Descent: Gradient Descent and Regularization Course: 54 Minutes Course Overview Types of Linear Regression Simple and Multiple Regression Implementing Simple Regression Implementing Multiple Regression Gradient Descent and Types Gradient Descent Optimization Algorithms Implementing Gradient Descent Implementing Mini Batch Gradient Descent Regularization Types Implementing L1 & L2 Regularization Exercise: Regression and Gradient Descent Online Mentor You can reach your Mentor by entering chats or submitting an email. Final Exam assessment Estimated duration: 90 minutes Practice Labs: Machine Learning Programming with Python (estimated duration: 8 hours) Perform ML programming tasks with Python, such as splitting data and standardizing data, and classification using nearest neighbors and ridge regression. Then, test your skills by answering assessment questions after performing principal component analysis, visualizing correlations, training a naive Bayes model and a support vector machine model. This lab provides access to several tools commonly used in ML, including: Microsoft Excel 2016, Visual Studio Code, Anaconda, Jupyter Notebook + JupyterHub, Pandas, NumPy, SiPy, Seaborn Library, Spyder IDE Track 2: Deep Learning Programmer In this track of the machine learning journey, the focus is neural networks, CNNs, RNNs, and ML algorithms. Content: E-learning courses Getting Started with Neural Networks: Biological & Artificial Neural Networks Course: 59 Minutes Course Overview Neural Network Fundamentals Biological Neural Network Artificial Neural Network Structure Neural Network Architecture Computational Models in Neural Networks Neurons Interconnection Threshold Functions and Artificial Neural Networks Implementing Neural Networks Building Neural Network Models Use Cases of Artificial Neural Network Exercise: Implement Neural Networks Getting Started with Neural Networks: Perceptrons & Neural Network Algorithms Course: 45 Minutes Course Overview Perceptrons Single Layer Perceptron Training Model Multilayer Perceptrons Linear and Non-Linear Functions Implement Perceptrons with Python Backpropagation Activation Functions Perceptron Classifier Exercise: Implement Perceptrons Building Neural Networks: Development Principles Course: 1 Hour, 21 Minutes Course Overview Artificial Neural Network Processing Components Learning and Training in Artificial Neural Network Cluster Analysis in Artificial Neural Network Neural Network Building Blocks Perceptron to Deep Neural Network Model and Hyperparameter Classification with Neural Networks Deep Learning Frameworks Neural Network Categorization Neural Network Computational Model Exercise: ANN Training and Classification Building Neural Networks: Artificial Neural Networks Using Frameworks Course: 1 Hour, 55 Minutes Course Overview Neural Network Building Components8 Evolutionary Algorithms and Gradient Descent Build Neural Networks Building Neural Networks with PyTorch Object Image Classification Learning Rates and Deep Learning Optimization Optimizing Speed Dense Network Tuning Using Hyperas Linear Model with Estimators Neural Network for Predictions Optimization Approach for Predictions Exercise: Build Neural Networks Training Neural Networks: Implementing the Learning Process Course: 1 Hour, 40 Minutes Course Overview Perceptrons and Neural Networks Perceptron Learning Algorithm Learning Rules in Neural Networks Supervised and Unsupervised Learning Neural Network Algorithms Data Preparation For Neural Networks ANN Training Process in Python Algorithms to Train Neural Networks Backpropagation in Python Classification Algorithm for Learning Regularization in Multilayer Perceptrons Exercise: Implement ANN Learning Training Neural Networks: Advanced Learning Algorithms Course: 1 Hour, 41 Minutes Course Overview Online and Offline Learning Training Patterns and Teaching Input Training Samples Baseline Overfitting and Underfitting L1 and L2 Regularization Training Neural Networks Pattern Association Training Algorithms Learning Vector Quantization Modified Hebbian Learning Hebbian Learning Rule Competitive Learning Optimizing Neural Networks Debugging Neural Networks Exercise: Implement Advanced Algorithms Improving Neural Networks: Neural Network Performance Management Course: 1 Hour, 57 Minutes Course Overview Iterative Machine Learning Workflow Hyperparameter Optimization Performance Management of Neural Networks Impact of Dataset Sizes on Neural Network Models Overfitting Prevention and Management Neural Network Problems and Solutions Bias and Variance Implementing Bias and Variance Trade Off Improving Performance Using Data and Algorithm Model Evaluation and Selection Exercise: Testing Models with Scikit-learn Privacy and Cookie PolicyTerms of Use Improving Neural Networks: Loss Function & Optimization Course: 1 Hour, 4 Minutes Course Overview Loss Function Impact of Loss Function Calculating Loss Function Causes of Optimization Problems Optimizer Algorithms Comparing Optimizer Algorithms Learning Rate Optimizations Implement Learning Rate Optimizer Exercise: Working with Loss Function Improving Neural Networks: Data Scaling & Regularization Course: 1 Hour, 38 Minutes Course Overview Optimizing Networks Rate Adaption Schedule Implementation with Keras Scaling and Scaling Methods Batch Normalization and Internal Covariate Shift Implementing Batch Normalization Implementing L1 Regularization Implementing L2 Regularization Implementing Gradient Descent Exercise: L1 Regularization and Gradient Descent ConvNets: Introduction to Convolutional Neural Networks Course: 1 Hour, 1 Minute Course Overview Convolutional Neural Network Use Cases How Convolutional Neural Network Works Types of Convolutional Neural Network Computer Vision Problems and Techniques Image Recognition and Classification Layers and Parameters of ConvNets Maths for Convolutional Neural Network Building CNN Image Classification Model Exercise: Working with Convolutional Neural Networks ConvNets: Working with Convolutional Neural Networks Course: 43 Minutes Course Overview NN Architecture and Softmax Classifier Working with ConvoNetJS Edge Detection Operations on Convolutions and Pooling Maths and Rules for Filter and Channel Detection Principles of Convolutional Layers Activation Layer and Comparing Activation Functions Improving Convolutional Neural Network Model Exercise: Edge Detection and CNN Improvement Convolutional Neural Networks: Fundamentals Course: 46 Minutes Course Overview Visual Signal Perception CNN Architecture Principles of CNN Sparse Interaction Shared Parameters and Spatial Extents Convolutional Padding and Strides Pooling Layers CNN and ReLU Semantic Segmentation Gradient Descent and its Variants Exercise: CNN Architecture and Principles Convolutional Neural Networks: Implementing & Training Course: 31 Minutes Course Overview Image Recognition ResNet Layers PyTorch Ecosystem Install and Configure PyTorch CNN Using PyTorch Training CNN Exercise: Implementing CNNs with PyTorch Convo Nets for Visual Recognition: Filters & Feature Mapping in CNN Course: 1 Hour, 7 Minutes Course Overview Convolutional Networks Convo Nets Architecture and Layers Filters and Their Usage Filters with Keras Feature Map Plotting Feature Map with Python Optimization Parameters Hyperparameters Tuning Tuning Hyperparameters with TensorFlow and Keras Pooling Layer Implementing Pooling Layer Exercise: Plotting Feature Map Convo Nets for Visual Recognition: Computer Vision & CNN Architectures Course: 49 Minutes Course Overview Activation Functions and Types1 Why ReLU in Convolutional Neural Networks Implementing ReLU Computer Vision Tasks Developing Object Photo Classification Model Fully-connected Layer Convolutional Neural Network Training Process Convolutional Neural Network Architectures Exercise: Applying ReLU in CNN Fundamentals of Sequence Model: Artificial Neural Network & Sequence Modeling Course: 36 Minutes Course Overview Artificial Neural Network (ANN) Components of ANN Modeling Tools and Frameworks Sequence Modeling Recurrent Neural Network (RNN) Types of RNN Build a RNN with PyTorch and Google Colab Exercise: ANN and Sequence Modeling Fundamentals of Sequence Model: Language Model & Modeling Algorithms Course: 19 Minutes Course Overview Language Model and NLP Sequence Generation for NLP Vanishing Gradient Problem Gated Recurrent Unit (GRU) Long Short-Term Memory (LSTM) Network Exercise: Language Modeling Build & Train RNNs: Neural Network Components Course: 37 Minutes Course Overview Artificial Neural Network Network Topologies Neuron Activation Mechanism Learning Samples Supervised, Unsupervised, and Reinforcement Training Samples Training Set and Pattern Recognition Gradient Optimization Procedure Exercise: Learning and Training Samples Build & Train RNNs: Implementing Recurrent Neural Networks Course: 49 Minutes Course Overview Perception and Backpropagation Single and Multilayer Perception Building Recurrent Neural Network Models RNN with Python and TensorFlow LSTM with TensorFlow Caffe2 and Neural Network Implement RNN with Caffe Deep Learning Language Model with Keras Exercise: Implement RNN Using TensorFlow and Caffe ML Algorithms: Multivariate Calculation & Algorithms Course: 39 Minutes Course Overview Multivariate Calculus Function Representation Gradient and Derivative Product and Chain Rule Partial Differentiation Linear Algebra Gradient and Jacobian Matrix Taylor's Theorem and Local Minima Exercise: Multivariate Operations for Calculus ML Algorithms: Machine Learning Implementation Using Calculus & Probability Course: 31 Minutes Course Overview Probability and Machine Learning Chain and Bayes Rules Variance and Random Vectors Estimation Parameters Deep Learning and Calculus R and Calculus Calculus in Python Series Expansion in Python Exercise: Derivatives and Integrals with SymPy Predictive Modeling: Predictive Analytics & Exploratory Data Analysis Course: 41 Minutes Course Overview Predictive Analytics Analytical Base Table Business Problems and Predictive Modeling Predictive Modeling with Python Exploratory Data Analysis Dataset and Variables Types Missing Values and Outlier Management Exercise: Predictive Modeling with Python Predictive Modeling: Implementing Predictive Models Using Visualizations Course: 42 Minutes Course Overview Feature Selection Algorithm Predictive Models Scatter Plots Pearson's Correlation Boxplot Boxplot Using Python Crosstab Using Python Statistical Concepts for Predictive Models Tree-Based Method Best Practices for Predictive Modeling Exercise: Implement Boxplots and Scatter Plots Online Mentor You can reach your Mentor by entering chats or submitting an email. Final Exam assessment Estimated duration: 90 minutes Practice Labs: Machine Learning Programming with Python (estimated duration: 8 hours) Perform ML programming tasks with Python, such as splitting data and standardizing data, and classification using nearest neighbors and ridge regression. Then, test your skills by answering assessment questions after performing principal component analysis, visualizing correlations, training a naive Bayes model and a support vector machine model. This lab provides access to several tools commonly used in ML, including: Microsoft Excel 2016, Visual Studio Code, Anaconda, Jupyter Notebook + JupyterHub, Pandas, NumPy, SiPy, Seaborn Library, Spyder IDE Track 3: Machine Learning Engineer In this track of the machine learning journey, the focus is predictive modeling and analytics, ml modeling, and ml architecting. Content: E-learning collections Predictive Modelling Best Practices: Applying Predictive Analytics Course: 1 Hour, 27 Minutes Course Overview The Predictive Modeling Process Statistical Concepts for Predictive Modeling Regression Techniques for Predictive Analytics Commonly Used Models for Predictive Analytics Survival Analysis for Customer Churn Market Basket Analysis Data Clustering Models Random Forests Probabilistic Graphical Models Classification Models Best Practices for Predictive Modeling Exercise: Applying Predictive Analytics Models Planning AI Implementation Course: 45 Minutes Course Overview Setting Expectations Challenges of AI The Importance of Training The Need for Data and Algorithms Understanding the Human Problem Developing Organizational Capability Management Challenges Avoiding AI Pitfalls Developing a Strategy Data Quality AI Needs and Tools Exercise: Describe AI Planning Considerations Automation Design & Robotics Course: 36 Minutes Course Overview Automation Overview Automation Targets Display Status Human-Computer Collaboration Human Intervention Software Testing Automation Task Runners in Software Design and Development DevOps and Automated Deployment Software Design Patterns for Robotics Process Automation Using Robotics Modern Robotics and AI Designs Exercise: Applying Automation and Robotics Design ML/DL in the Enterprise: Machine Learning Modeling, Development, & Deployment Course: 1 Hour, 5 Minutes Course Overview Challenges of Machine Learning Machine Learning Process Stages Machine Learning Development Lifecycle Machine Learning Workflow Machine Learning Training Process Machine Learning Platforms Machine Learning Data Modelling and Processing H2O Machine Learning Environment Data Source Management Machine Learning Pipeline Git Code Movement Exercise: Machine Learning Training Processes ML/DL in the Enterprise: Machine Learning Infrastructure & Metamodel Course: 54 Minutes Course Overview Infrastructure for Data and Process Machine Learning and Data Pipeline Machine Learning Models Machine Learning Visualization Machine Learning Frameworks and Tools Working with H Model Metadata and Governance Risk Mitigation Exercise: Build Data Pipelines and Visualization Enterprise Services: Enterprise Machine Learning with AWS Course: 1 Hour, 14 Minutes Course Overview Cloud and Machine Learning Machine Learning Workflow Comparison AWS Machine Learning Tools and Capabilities Cloud Machine Learning Implementation Comparison Generating Machine Learning Objects and Prediction Amazon Machine Learning Console Amazon SageMaker Architecture Using Amazon SageMaker Lex, Polly, and Transcribe Amazon SageMaker Neo Augmented Manifest in Amazon SageMaker Amazon SageMaker Model Tuning Amazon SageMaker Automatic Tuning Course Summary Enterprise Services: Machine Learning Implementation on Microsoft Azure Course: 1 Hour, 13 Minutes Course Overview Azure Machine Learning Tools and Capabilities Comparing Azure ML Studio and Azure ML Service Creating & Configuring Azure ML Service Workspace Building ML Pipelines with Azure ML Service Working with Azure ML Studio Using Azure ML Service Visual Interface Working with Azure Open Datasets Azure MLOps Azure ML R Notebooks Pipelines with Azure Data Lake and Azure ML CI/CD for Machine Learning with Azure Pipeline Using Microsoft DevLabs Extension Course Summary Enterprise Services: Machine Learning Implementation on Google Cloud Platform Course: 1 Hour, 2 Minutes Course Overview GCP Machine Learning Tools and Capabilities Google Cloud Platform ML Capabilities Training and Job Execution with GCloud and Console BigQuery and BigQuery ML Features Implementing Models with BigQuery ML ML Workflow Challenges and Serverless Approach ML Implementation with Cloud Datalab Google AI Platform Features and Components Google Cloud AutoML Features Managing Dataset Using AutoML Tables Training Models and Predicting with AutoML Tables Google Cloud AutoML Natural Language Course Summary Architecting Balance: Designing Hybrid Cloud Solutions Course: 57 Minutes Course Overview Cloud Features and Deployment Models Comparative Analysis of On-prem and Cloud Models Factors Influencing On-premise & Cloud Architecture Hybrid vs. Private vs. Public Cloud Hybrid Cloud Need Assessment Hybrid Cloud Strategy and Architecture Hybrid Cloud Benefits Challenges of Implementing Hybrid Cloud Application Deployment Strategy Setting up Hybrid Cloud Architecture Exercise: Benefits of Hybrid Cloud Enterprise Architecture: Architectural Principles & Patterns Course: 1 Hour, 35 Minutes Course Overview Software Architecture Concepts Software Architecture Principles Architectural Models and Views Software Architecture Styles Principles of Developing Enterprise Architecture Architectural Principles for Data and Technology SOA Principles and the Maturity Model Serverless Architecture Backend-as-a-Service Evolutionary Architecture Documenting Architecture Project Team and Collaboration Consumer-Driven Contracts Dimensions of Architecture to Maximize Benefit Software Architecture Actions Architectural Patterns and Styles Course Summary Enterprise Architecture: Design Architecture for Machine Learning Applications Course: 1 Hour Course Overview Architecture for ML in Enterprises Software Architecture to Model ML Apps in Production Model Machine Learning Apps ML Reference Architecture and Building Blocks Evolvable Architectures and Migration Pitfalls of Evolutionary Architecture and Antipatterns Setting Up ML Solutions Fitness Function and Categories Architecture for Refinement and Production Readiness Course Summary Architecting Balance: Hybrid Cloud Implementation with AWS & Azure Course: 1 Hour, 8 Minutes Course Overview Use Cases of AWS Hybrid Cloud AWS Services for Hybrid Cloud Implementation Cloudbursting Application Hosting Model AWS Services for Resource and Deployment Management Hybrid Data Lake Implementation Principles and Best Practices of AWS Hybrid Azure Components for Hybrid Solutions Azure Hybrid Services Azure Stack Azure Tooling and DevOps for Hybrid Cloud Azure Stack Implementation Exercise: Implement Hybrid Cloud with Azure Refactoring ML/DL Algorithms: Techniques & Principles Course: 1 Hour, 6 Minutes Course Overview Role of Refactoring Technical Debts Refactoring Techniques PyCharm for Refactoring Code Analysis and Refactoring Design Principles Refactoring Principles and Challenges Principles of Good Code Refactoring Python Code Code Optimization Using Rope to Refactor Anti-patterns in Code Exercise: Refactoring Techniques Refactoring ML/DL Algorithms: Refactor Machine Learning Algorithms Course: 59 Minutes Course Overview Machine Learning Types Machine Learning Algorithm Design Impact of Refactoring on Machine Learning Algorithm Design Machine Learning Algorithm Comparison Refactor Machine Learning Code Managing Technical Debt in Machine Learning SonarQube and Code Coverage Automatic Clone Refactoring Exercise: Refactoring Machine Learning Code Online Mentor You can reach your Mentor by entering chats or submitting an email. Final Exam assessment Estimated duration: 90 minutes Practice Labs: Architecting ML/DL Apps with Python (estimated duration: 8 hours) Perform architecting tasks such as binning data, imputing values, performing cross validation, and evaluating a classification model. Then, test your skills by answering assessment questions after validating a model, tuning parameters, refactoring a machine learning model, and saving and loading models using Python. Track 4: Machine Learning Architect In this track of the machine learning journey, the focus is applied predictive modeling, CNNs and RNNs, and ML algorithms. Content: E-learning collections Applied Predictive Modeling Course: 1 Hour, 8 Minutes Course Overview Overview of Predictive Modeling Exploratory Data Analysis Overview of Regression Methods Linear Regression in Python Logistic Regression in Python Overview of Clustering Methods Hierarchical Clustering in Python K-Means Clustering in Python Overview of Decision Trees and Random Forests Decision Trees in Python Random Forests in Python Exercise: Apply Predictive Models Implementing Deep Learning: Practical Deep Learning Using Frameworks & Tools Course: 1 Hour Course Overview Comparing DL and ML ML/DL Workflow Deep Learning Network Components DL/ML Frameworks Recurrent CNN with Caffe Autoencoders and PyTorch Deep Neural Network Implementation Platform and Framework Comparison Selecting the Right ML/DL Frameworks Challenges of Debugging Deep Learning Networks Exercise: Using DL Frameworks and Tools Implementing Deep Learning: Optimized Deep Learning Applications Course: 43 Minutes Course Overview Computational Graph and Deep Learning Accelerating Architectures GPU Interfaces TFX and Pipeline Components for ML Pipelines Setting up TFX Build TFX Pipeline Using TFMA Practical Consideration for DL Build and Train Deep Learning Parameters Exercise: Optimizing Deep Learning Applications Applied Deep Learning: Unsupervised Data Course: 1 Hour, 28 Minutes Course Overview Deep Learning to Model NLP and Audio Analysis Recurrent Neural Network Architectures Unsupervised Learning Challenges in Deep Learning Generative and Discriminative Classifiers Types of Generative Models PixelCNN Setup Differences between MLP, CNN, and RNN ResNet for Computer Vision Encoders and Autoencoders Exercise: RNN and ResNet Applied Deep Learning: Generative Adversarial Networks and Q-Learning Course: 45 Minutes Course Overview Implement Autoencoder Using Keras Implementing Generative Adversarial Networks Building GAN Model Using Python and Keras Generative Adversarial Network Challenges Deep Reinforcement Learning Deep RL and Deep Learning Comparison Generative Adversarial Network Variations Deep Q-Learning Deep Q-Learning in Python Exercise: Implementing GAN and Deep Q-Learning Advanced Reinforcement Learning: Principles Course: 1 Hour, 13 Minutes Course Overview Reinforcement Learning Concepts Comparing Reinforcement and Machine Learning Reinforcement Learning Use Cases Reinforcement Learning Terms and Workflow Reinforcement Learning Implementation Approaches Reinforcement Learning Algorithms Markov Decision Process and Its Variants Markov Reward Process and Value Functions Markov Decision Process Toolbox Capabilities Exercise: Reinforcement Learning and MDP Toolbox Advanced Reinforcement Learning: Implementation Course: 1 Hour, 35 Minutes Course Overview Installing the Markov Decision Process Toolbox Rewards and Discounts Multi-Armed Bandit Problem Dynamic Programming and Bellman Equation Reinforcement Learning Agent and Its Components Reinforcement Learning with OpenAI Gym and Keras Reinforcement Learning Taxonomy by OpenAI Deep Q-Learning Implementation Training DNN Using Reinforcement Learning Exercise: Implementing Deep Q-Learning ML/DL Best Practices: Machine Learning Workflow Best Practices Course: 53 Minutes Course Overview Installing the Markov Decision Process Toolbox Rewards and Discounts Multi-Armed Bandit Problem Dynamic Programming and Bellman Equation Reinforcement Learning Agent and Its Components Reinforcement Learning with OpenAI Gym and Keras Reinforcement Learning Taxonomy by OpenAI Deep Q-Learning Implementation Training DNN Using Reinforcement Learning Exercise: Implementing Deep Q-Learning ML/DL Best Practices: Building Pipelines with Applied Rules Course: 1 Hour, 4 Minutes Course Overview Troubleshooting Deep Learning and Using Checklists ML Technical Challenges and Best Practices Case Study to Analyze Impacts of Best Practices Deployment Challenges and Patterns Case Study of Deployment Approaches Architecting and Building ML Pipelines Rules for Building Machine Learning Pipelines Feature Engineering Rules Training-Serving Skew Rules for Managing Optimization Refinement ML Project Checklists for Project Managers Course Summary Research Topics in ML and DL Course: 42 Minutes Course Overview Prevent Neural Networks from Overfitting Multi-Label Learning Algorithms Deep Residual Learning for Image Recognition Transferable Features in Deep Neural Networks Large-Scale Video Classification Common Objects in Context Generative Adversarial Nets Scalable Nearest Neighbor Algorithms Face Alignment with Ensemble of Regression Trees Learning Deep Features for Scene Recognition Extreme Learning Machine (ELM) Exercise: Recognize Research Topics in ML and DL Deep Learning with Keras Course: 1 Hour, 56 Minutes Course Overview Neural Networks Introduction to Keras Keras Backend Set up Keras Model Types in Keras Keras Layers Regression Classification Image Classification Keras Metrics Jupyter Notebooks Dataset for Neural Network Explore Your Dataset Data Preparation Compiling the Model Training and Testing Neural Networks Evaluate the Model Making Predictions Exercise: Using a Neural Network Online Mentor You can reach your Mentor by entering chats or submitting an email. Final Exam assessment Estimated duration: 90 minutes Practice Labs: Architecting Advanced ML/DL Apps with Python (estimated duration: 8 hours) Perform advanced ML/DL app architecture tasks using Python, such as loading a data set to train a simple multilayer perceptron (MLP), a Convolutional Neural Network (CNN) and an LSTM model. Then, test your skills by answering assessment questions after performing image and text classification using CNN. Specificaties Taal: Engels Kwalificaties van de Instructeur: Gecertificeerd Cursusformaat en Lengte: Lesvideo's met ondertiteling, interactieve elementen en opdrachten en testen Lesduur: 100 uur Assesments: De assessment test uw kennis en toepassingsvaardigheden van de onderwerpen uit het leertraject. Deze is 365 dagen beschikbaar na activering. Online mentor: U heeft 24/7 toegang tot een online mentor voor al uw specifieke technische vragen over het studieonderwerp. De online mentor is 365 dagen beschikbaar na activering, afhankelijk van de gekozen Learning Kit. Online Virtuele labs: Ontvang 12 maanden toegang tot virtuele labs die overeenkomen met de traditionele cursusconfiguratie. Actief voor 365 dagen na activering, beschikbaarheid varieert per Training. Voortgangsbewaking: Ja Toegang tot Materiaal: 365 dagen Technische Vereisten: Computer of mobiel apparaat, Stabiele internetverbindingen Webbrowserzoals Chrome, Firefox, Safari of Edge. Support of Ondersteuning: Helpdesk en online kennisbank 24/7 Certificering: Certificaat van deelname in PDF formaat Prijs en Kosten: Cursusprijs zonder extra kosten Annuleringsbeleid en Geld-Terug-Garantie: Wij beoordelen dit per situatie Award Winning E-learning: Ja Tip! Zorg voor een rustige leeromgeving, tijd en motivatie, audioapparatuur zoals een koptelefoon of luidsprekers voor audio, accountinformatie zoals inloggegevens voor toegang tot het e-learning platform.

Wereldwijd marktleider in Lean Six Sigma. Deze training is voor Green Belts die een project op Black Belt niveau willen realiseren en daarmee een internationaal certificaat behalen.  In deze GB2BB training draait alles om het realiseren van een succesvol Lean Six Sigma Black Belt-project, inclusief aantoonbaar resultaat. Je volgt drie dagen theorie, en daarna start het echte werk: jij voert een verbeterproject uit binnen jouw organisatie, met onbeperkte persoonlijke coaching van onze experts.    Het resultaat?✅ Een internationaal erkend Black Belt-certificaat✅ Waardevolle praktijkervaring op het hoogste Lean Six Sigma niveau✅ Een besparing voor jouw organisatie van gemiddeld 30 tot 50 keer de investering in deze training   Persoonlijke begeleiding die écht werktWat ons onderscheidt van andere aanbieders is onze persoonlijke aanpak. Jij krijgt 1-op-1 coaching, afgestemd op jouw werksituatie, uitdagingen en leerstijl. Want iedere deelnemer is anders en daar zijn wij op ingesteld.    De theorie? Die houden we kort en to-the-point. Alles wat je nodig hebt om een vliegende start te maken. En is je Green Belt-kennis wat weggezakt? Geen zorgen: je krijgt toegang tot al het lesmateriaal in onze online omgeving, zodat je alles in je eigen tempo kunt opfrissen.   Kortom: geen papieren training, maar een praktisch traject dat leidt tot écht succes.  De training bestaat uit 3 theorie trainingsdagen. Waarin de focus ligt op Six Sigma en het gebruik van Minitab. Dag 1 - Opfrissen (recap Green Belt theoriecertificering) Op de eerste dag wordt ingegaan op de belangrijkste onderwerpen van de Six Sigma structuur.  Onderwerpen als: DPMO Vitale X en de stratificatie facturen DMAIC structuur Basis Statistiek Dag 2 en 3 - statistiek en Minitab De volgende 2 dagen gaan onder andere in op: Beschrijvende statistiek Inferentiële statistiek Minitab Hypothese testen Statistische proces controle Aan het einde van de dag vindt het Black Belt voor de dienstverlening examen plaats.  Praktijkopdracht Na theorie volgt de praktijkopdracht. Onder begeleiding van een Master Black Belt voert u deze opdracht uit met als eindresultaat een geoptimaliseerd proces volgens de Lean Six Sigma methode. Een project levert typisch tussen de €50.000 en €500.000 besparing op.  Neem contact met ons op  m.b.t. informatie over de praktijkopdracht.