Opleiding: Machine Learning Practitioner

Machine Learning Practitioner.

The Machine Learning Practitioner Training: LearningKit prepares professionals to build, optimize, deploy, and manage modern machine learning systems using today's most widely adopted AI engineering practices and tools.

This comprehensive LearningKit follows the complete machine learning lifecycle—from feature engineering and model optimization to production deployment and continuous monitoring. You'll develop expertise in data preprocessing, feature selection, dimensionality reduction, hyperparameter optimization, anomaly detection, MLOps, model serving, CI/CD automation, and reinforcement learning.

The curriculum provides extensive hands-on experience with advanced machine learning technologies including scikit-learn, Hyperopt, Ray Tune, Optuna, H2O AutoML, TensorFlow Keras Tuner, MLflow, Databricks, and modern deployment strategies such as batch, online, streaming, edge, blue/green, and A/B deployments. You'll also explore reinforcement learning concepts, implementing intelligent agents with Q-learning, SARSA, and Deep Q-Networks to solve dynamic decision-making problems.

Ideal for machine learning engineers, data scientists, AI developers, MLOps engineers, platform engineers, and technical professionals, this LearningKit provides the advanced technical expertise required to transform machine learning models into reliable, scalable, and production-ready AI solutions that deliver measurable business value.

This LearningKit with more than 21 hours of learning is divided into three tracks:

Course Outcome

Section 1: Feature Engineering for ML Models

This section lays the foundation for high-performing machine learning models by focusing on data preparation and feature engineering. You will learn how to handle missing values and outliers, encode categorical variables and scale numeric features so that data is model-ready.

Handling Missing Values and Outliers in Data

Course: 1 Hour, 22 Minutes

• Course Overview
• Data Preparation
• Missing Values and Outliers
• Outlier Identification and Analysis
• Outlier Detection and Management
• Loading and Preparing Data for Cleaning
• Dropping Records and Imputing Values to Address Missing Data
• Implementing Multivariate Imputation
• Detecting and Visualizing Outliers with Box Plots
• Identifying and Computing Outliers Using IQR
• Capping Outliers with IQR and Evaluating Results
• Using the Z-Score Technique for Outlier Detection and Capping
• Course Summary

Encoding Categorical Data for Machine Learning

Course: 54 Minutes

• Course Overview
• Data Types and Processing Techniques
• One-Hot Encoding of Categorical Data
• Encoding Methods for Categorical Data
• Techniques for Encoding Categorical Variables
• Performing Label Encoding of Categorical Values
• Performing One-Hot Encoding of Nominal Categorical Variables
• Encoding Categorical Variables with the Ordinal Encoder
• Discretizing Variables and Training a Random Forest Model
• Course Summary

Scaling Numeric Data for Machine Learning

Course: 37 Minutes

• Course Overview
• Scaling Numeric Features
• Scaling Techniques and Their Applications
• Applying Min-Max Scaling to Numeric Features
• Implementing Standard Scaling and Frequency Encoding
• Training a KNN Classifier and Evaluating Performance
• Course Summary

Feature Engineering Techniques for Machine Learning

Course: 1 Hour, 7 Minutes

• Course Overview
• Feature Engineering Features and Mitigating Overfitting Risks
• Feature Creation and Model Impact
• Synthesizing Features and Constructing Scikit-Learn Pipelines
• Executing Pipeline Fit, Transformations, and Model Evaluation
• Performing Feature Engineering
• Modeling Non-Linear Relationships with Polynomial Features
• Creating Polynomial Features for a Regression Model
• Performing Log Transformations on Features
• Implementing Principal Component Analysis
• Applying Power Transformations and Performing PCA
• Course Summary

Feature Selection and Dimensionality Reduction

Course: 1 Hour, 11 Minutes

• Course Overview
• Feature Selection Methods
• Feature Selection Techniques
• Dimensionality Reduction with Principal Component Analysis (PCA)
• Analyzing Multicollinearity and Performing Feature Selection
• Performing Feature Selection Using Variance Threshold and the F-Statistic
• Applying Mutual Information Regression to Enhance Feature Selection
• Implementing Model-Based Feature Selection with Ridge Regression
• Executing Sequential Feature Selection
• Performing Classification Feature Selection Using Chi-Squared and F-Statistic
• Executing Recursive Feature Selection
• Course Summary

Section 2: Hyperparameter Tuning for Machine Learning

This section focuses on hyperparameter tuning, one of the most important drivers of model performance. You will learn the difference between model parameters and hyperparameters and explore techniques such as grid search, random search and cross-validation.

Hyperparameter Tuning Techniques

Course: 1 Hour, 12 Minutes

• Course Overview
• Model Parameters and Hyperparameters
• Hyperparameter Tuning
• Hyperparameter Tuning and Early Stopping Techniques
• Cross-Validation to Mitigate Overfitting
• K-Fold Cross-Validation
• Decision Trees and Hyperparameter Tuning
• Balancing Hyperparameters in Decision Trees
• Regularization and Splitting in Decision Trees
• Hyperparameter Tuning Algorithms
• Hyperparameter Tuning Methods
• Course Summary

Hyperparameter Tuning with scikit-learn

Course: 1 Hour, 51 Minutes

• Course Overview
• Hyperparameter Tuning with scikit-learn
• Performing Exploratory Data Analysis and Visualizing Correlations
• Building a Baseline Regression Model and Evaluating Performance
• Tuning Decision Tree Hyperparameters
• Executing GridSearchCV to Tune Regression Models
• Utilizing GridSearchCV Results and Analyzing Best Models
• Implementing and Evaluating RandomizedSearchCV
• Employing HalvingGridSearchCV for Hyperparameter Tuning
• Using HalvingGridSearchCV with Dynamic Resources
• Selecting Optimal Regression Models Through Hyperparameter Tuning
• Building a Baseline Classification Model
• Implementing Hyperparameter Tuning with GridSearchCV for Classification Models
• Performing GridSearchCV with Multiple Scoring Metrics
• Exploring Randomized and Halving Random Search Strategies
• Course Summary

Hyperparameter Tuning with Hyperopt on Databricks

Course: 1 Hour, 57 Minutes

• Course Overview
• The Databricks Platform
• Hyperopt on Databricks
• Search Algorithms in Hyperopt
• Creating a Databricks Workspace on Azure
• Configuring and Launching an Apache Spark Cluster
• Creating a Databricks Volume and Uploading CSV Data
• Writing Python Code Using Databricks Notebooks
• Encoding and Scaling Data and Training a Model
• Tracking a Model with MLflow and Exploring Experiment Data
• Executing and Analyzing Hyperparameter Tuning with Hyperopt
• Analyzing Hyperparameter Tuning Runs in Databricks
• Performing Distributed Hyperparameter Tuning with SparkTrials
• Course Summary

Hyperparameter Tuning with Ray Tune on Databricks

Course: 50 Minutes

• Course Overview
• Key Features of Ray Tune
• Performing Hyperparameter Tuning with Ray Tune
• Understanding Ray Tasks and MLflow Integration
• Executing Parallel Model Training and Logging with Ray
• Executing Hyperparameter Tuning with Ray Tune
• Course Summary

Hyperparameter Tuning with Optuna

Course: 59 Minutes

• Course Overview
• Hyperparameter Optimization with Optuna
• Training a Baseline Classification Model and Exploring the Confusion Matrix
• Running an Optuna Study to Find the Best Hyperparameters
• Interpreting an Optuna Study
• Visualizing Hyperparameter Tuning Using Optuna Dashboard
• Hyperopt vs. Optuna vs. Ray Tune
• Course Summary

Automated Machine Learning with H2O AutoML

Course: 54 Minutes

• Course Overview
• H2O AutoML
• Training Regression Models with H2O AutoML
• Automatically Tuning Multiple Models Using H2O AutoML
• Training Models with Diverse Algorithms Using H2O AutoML
• Interpreting Models with H2O’s Explain Function
• Course Summary

Hyperparameter Tuning with Keras Tuner

Course: 47 Minutes

• Course Overview
• Hyperparameter Optimization with Keras Tuner
• Utilizing Hyperparameter Tuning with Keras Tuner in Colab
• Training a Convolutional Network for Image Classification
• Tuning Hyperparameters with Keras Tuner
• Visualizing and Evaluating Hyperparameter Tuning with TensorBoard
• Course Summary

Section 3: Anomaly Detection

This section equips you with practical techniques to identify outliers, errors, fraud patterns and rare events that can affect data quality and model performance.

Understanding Anomalies and Their Detection

Course: 49 Minutes

• Course Overview
• What Are Anomalies?
• Point Anomalies
• Contextual Anomalies
• Collective Anomalies
• Course Summary

Using Z-Scores and IQR for Anomaly Detection

Course: 47 Minutes

• Course Overview
• Anomaly Detection with Z-Scores
• Anomaly Detection with Interquartile Ranges (IQR)
• Anomaly Detection with Modified Z-Scores
• Using Interquartile Ranges to Identify Anomalies
• Using Z-Scores and Modified Z-Scores for Anomaly Detection
• Course Summary

Using LOF, iForest, and One-Class SVMs for Anomaly Detection

Course: 1 Hour, 43 Minutes

• Course Overview
• Anomaly Detection with Local Outlier Factor (LOF)
• Performing Anomaly Detection Using LOF
• Configuring the Sensitivity of LOF Using Number of Neighbors
• Anomaly Detection with Isolation Forest (iForest)
• Performing Anomaly Detection Using iForest
• Visualizing and Interpreting Anomalies Using Scatter Plots and Histograms
• Anomaly Detection with One-Class Support Vector Machines (OC SVMs)
• Performing Anomaly Detection with One-Class SVM and a Linear Kernel
• Performing Anomaly Detection with One-Class SVM and an RBF Kernel
• Anomaly Detection with Elliptic Envelope Detection
• Performing Anomaly Detection with Elliptic Envelope
• Course Summary

Section 4: MLOps and Model Deployment

This section bridges the gap between model development and production. You will learn how MLOps extends DevOps practices to address machine learning-specific challenges such as data drift, model retraining, reproducibility, lifecycle management, CI/CD, infrastructure as code, containerization and automated testing.

MLOps and Model Deployment: Model Deployment and Serving Strategies

Course: 1 Hour, 18 Minutes

• Course Overview
• Model Deployment and Serving in MLOps
• Batch, Online, Streaming, and Edge Serving Modes
• Big Bang, Blue/Green, and A/B Deployment Strategies
• Model Hosting and Compute Choices
• Scalability Pattern and Update Frequency
• Model Servers and Managed ML Serving Platforms
• MLflow
• Information Captured in MLflow Runs
• Model Registry
• Course Summary

MLOps and Model Deployment: Contextualizing MLOps and DevOps

Course: 52 Minutes

• Course Overview
• Introducing MLOps
• CI/CD in MLOps
• IaC in MLOps
• Containerization and Orchestration in MLOps
• Automated Testing and Version Control in MLOps
• DevOps vs. MLOps
• Course Summary

MLOps and Model Deployment: Training and Deploying Models Using MLflow on Databricks

Course: 46 Minutes

• Course Overview
• Setting Up the Machine Learning Environment on Databricks
• Splitting and Preprocessing the Data for Machine Learning
• Training and Tracking Parameters and Metrics Using MLflow Runs
• Registering a Model with the MLflow Registry
• Deploying and Serving the Model
• Course Summary

Section 5: Reinforcement Learning

This section introduces reinforcement learning, a machine learning paradigm in which agents learn optimal behavior through trial, feedback and reward instead of labeled data.

Implementing Simple Reinforcement Learning Methods

Course: 44 Minutes

• Course Overview
• Set Up an Environment for a Reinforcement Learning Agent
• Instantiating and Training an Agent
• Visualizing Metrics for a Trained Agent
• Setting Up an Environment and Agent for Shortest Path Computation
• Visualizing Metrics for the Trained Shortest Path Agent
• Course Summary

Contextualizing Reinforcement Learning

Course: 58 Minutes

• Course Overview
• Contextualizing Reinforcement Learning
• Actions and States
• Environment Modeling and MDP
• Policy Search
• Dynamic Programming and Q-Learning Intuition
• Q-Learning and SARSA
• Deep Q Networks
• Course Summary

Specificaties
Taal: Engels
Kwalificaties van de Instructeur: Gecertificeerd
Cursusformaat en Lengte: Lesvideo's met ondertiteling, interactieve elementen en opdrachten en testen
Lesduur: 21:36 uur
Assesments: De assessment test uw kennis en toepassingsvaardigheden van de onderwerpen uit het leertraject. Deze is 365 dagen beschikbaar na activering.
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.
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.
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.