Opleiding: AI+ Developer Practitioner™

Formerly known as AI+ Developer™

Get hands-on with the tools and technologies that power the AI ecosystem.

• Core AI Foundations: Covers Python, deep learning, data processing, and algorithm design

• Hands-on Projects: Focus on NLP, computer vision, and reinforcement learning

• Advanced Modules: Includes time series, model explainability, and cloud deployment

• Industry-Ready Skills: Prepares learners to design and deploy complex AI systems

Software Developers: Enhance your coding expertise by mastering AI algorithms and deep learning techniques.

Data Enthusiasts: Apply AI-driven data analysis, machine learning models, and deep learning to solve complex problems.

Computer Vision & NLP Researchers: Dive into specialized AI fields, including computer vision and natural language processing.

IT Specialists & System Architects: Integrate AI solutions into existing systems and optimize performance.

Students & Fresh Graduates: Build a strong foundation in AI development and prepare for future opportunities in tech.

• Master Key AI Development Skills:Learn Python, deep learning, advanced concepts, and optimization techniques to build robust AI solutions.
• Specialize in Cutting-Edge AI Domains:Gain expertise in NLP, computer vision, or reinforcement learning, alongside data processing, exploratory analysis, and time series analysis.
• Stay Ahead in AI Development:AI is transforming industries, and organizations seek developers with strong proficiency in deploying AI models to solve real-world problems.
• Advance Your Career in AI Development:With growing demand across tech, finance, and healthcare sectors, this certification positions you as a leader in AI-driven development.

• 1.1 Understanding Artificial Intelligence
• 1.2 Components of an AI Application
• 1.3 Beginner AI Development Workflow
• 1.4 AI Development Concepts and Limitations
• 1.5 Case Study: A Chatbot Prototype That Produced Unreliable Answers
• 1.6 Use Case: Selecting the Right AI Approach

• 2.1 Python Foundations
• 2.2 Python Data Structures and File Handling
• 2.3 Beginner Software-Development Practices
• 2.4 Case Study: An Unstructured Python Script Becomes Difficult to Maintain
• 2.5 Use Case: Automated File Processing Utility

• 3.1 Working with NumPy and Pandas
• 3.2 Data Cleaning
• 3.3 Exploratory Data Analysis
• 3.4 Case Study: Dirty Customer Data Produces Incorrect Sales Insights
• 3.5 Use Case: Retail Sales Data Preparation

• 4.1 Essential Mathematical Concepts
• 4.2 Essential Statistics
• 4.3 Mathematical Reasoning for AI
• 4.4 Case Study: Average Performance Hides a Major Customer Problem
• 4.5 Use Case: Similarity-Based Product Recommendation

• 5.1 Understanding Machine Learning
• 5.2 Supervised Machine Learning
• 5.3 Unsupervised and Other Beginner Methods
• 5.4 Case Study: Customer Churn Prediction
• 5.5 Use Case: Delivery-Time Prediction

• 6.1 Model Evaluation Metrics
• 6.2 Improving Model Performance
• 6.3 Reliable Model Delivery
• 6.4 Case Study: A High-Accuracy Model Misses the Important Cases
• 6.5 Use Case: Spam Email Detection

• 7.1 Neural Network Fundamentals
• 7.2 Beginner Deep Learning with PyTorch
• 7.3 Computer Vision Foundations
• 7.4 Case Study: Manufacturing Defect Detection with Transfer Learning
• 7.5 Use Case: Product Image Classification

• 8.1 Text Processing Fundamentals
• 8.2 Embeddings and Transformers
• 8.3 Large Language Model Fundamentals
• 8.4 Case Study: Choosing Between a Text Classifier and an LLM for Routing Support Tickets
• 8.5 Use Case: Customer Review Analysis

• 9.1 Prompt Engineering Foundations
• 9.2 Building Controlled Generative AI Applications
• 9.3 Multimodal AI Foundations
• 9.4 Case Study: Invoice Extraction Produces Incorrect Financial Fields
• 9.5 Use Case: Multimodal Product Information Assistant

• 10.1 Retrieval Fundamentals
• 10.2 Building a Basic RAG Workflow
• 10.3 RAG Quality and Control
• 10.4 Case Study: A Policy Assistant Returns an Outdated Rule
• 10.5 Use Case: Employee Handbook Assistant

• 11.1 API Development for AI
• 11.2 Basic AI Agents and Tool Use
• 11.3 Beginner Deployment and Operations
• 11.4 Case Study: An Over-Privileged Agent Performs an Unapproved Action
• 11.5 Use Case: IT Support Triage Assistant

• 12.1 Responsible AI Foundations
• 12.2 AI Application Security
• 12.3 Monitoring and Production Readiness
• 12.4 Case Study: Prompt Injection Causes Confidential Data Exposure
• 12.5 Use Case: Beginner AI Release Checklist

• 1.1 What Are AI Agents?
• 1.2 Significance of AI Agents for Developers
• 1.3 Applications and Trends of AI Agents for Developers
• 1.4 How Does an AI Agent Work?
• 1.5 Core Characteristics of AI Agents
• 1.6 Importance of AI Agents
• 1.7 Types of AI Agents
• 1.8 Comparison Table of AI Agents in Ethics

• GitHub Copilot
• Lobe
• H2O.ai
• Snorkel

Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam

Access to all materials and exams is provided for 365 days after delivery.

De OCICT maatwerk-methode houdt in dat op basis van de behoefte van de klant - en indien nodig na een intakegesprek - een individueel opleidingstraject wordt opgesteld. De trainingen worden veelal individueel gevolgd, zodat iedere training een maatwerktraject is. Klassikale trainingen zijn natuurlijk mogelijk, mits ze aansluiten bij de leerbehoeften van de deelnemers / opdrachtgever.

Alle onderdelen van de training kennen een voortdurende wisselwerking tussen theorie en praktijk. De inhoud van de training kan vooraf per cursist worden aangepast en tijdens de cursus worden bijgesteld. De focus ligt op maatwerk: het traject wordt afgestemd op de doelstellingen van de deelnemer(s) en/of opdrachtgever, ongeacht of het individueel of klassikaal wordt aangeboden.