• 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

Module 1: Foundations of Modern AI for Developers

• 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

Module 2: Python Programming for AI

• 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

Module 3: Data Handling and Visualization

• 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

Module 4: Practical Mathematics and Statistics for AI

• 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

Module 5: Machine Learning Fundamentals

• 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

Module 6: Model Evaluation and Improvement

• 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

Module 7: Deep Learning and Computer Vision Basics

• 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

Module 8: Natural Language Processing, Transformers, and LLM Fundamentals

• 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

Module 9: Generative and Multimodal AI Application Development

• 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

Module 10: Retrieval-Augmented Generation and Knowledge Assistants

• 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

Module 11: Simple AI Agents, APIs, and Deployment

• 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

Module 12: Responsible AI, Security, Monitoring, and Capstone

• 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

Optional Module: AI Agents for Developer

• 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

AI Machine Learning Developer

Design, implement, and optimize algorithms and models to enable systems to learn from data and make predictions or decisions.

AI Solutions Architect

Design and implement AI systems that integrate seamlessly with existing infrastructure to address business needs effectively and enhance system capabilities.

AI Application Developer

Build, design, and maintain AI-driven applications that solve real-world problems, integrating AI technologies for enhanced functionality.

AI System Programmers

Develop and maintain AI systems, including programming algorithms and software components that enable intelligent behavior in machines and applications.