Practical Python Artificial Intelligence Programming

Course overview

Artificial Intelligence programming with Python. Covers both theory and practical examples of LLMs, deep learning, symbolic AI, and Knowledge Representation

32 chapters
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Practical Python Artificial Intelligence Programming32 chapters

Begin
  1. Cover Material, Copyright, and License

  2. Preface

  3. Python Development Environment

  4. Part 1 - Machine Learning

  5. “Classic” Machine Learning

  6. Regression and Clustering

  7. Exploratory Data Analysis and Feature Engineering

  8. Anomaly Detection

  9. Part 2 - Deep Learning

  10. The Basics of Deep Learning

  11. Natural Language Processing Using Deep Learning

  12. Part IV - Overviews of Image Generation, Reinforcement Learning, and Recommendation Systems

  13. Overview of Image Generation

  14. Categorical Deep Learning: From Axioms to Implementation using Category Theory

  15. Overview of Reinforcement Learning (Optional Material)

  16. Overview of Recommendation Systems (Optional Material)

  17. Part 3 - Large Language Models

  18. Introduction to Transformers and Large Language Models

  19. LLMs with Public APIs

  20. LLMs with Local Models

  21. Text Adventure Game with an LLM Game Master

  22. Semantic Web Question Answering with SPARQL and Large Language Models

  23. Object Oriented AI Agents with NVIDIA’s OO Agents Framework

  24. Part 4 - Symbolic AI and Knowledge Representation

  25. Symbolic AI

  26. Expert Systems Using the Rete Algorithm

  27. A Chess Engine and AI Player in Pure Python

  28. Part 5 - Knowledge Representation

  29. Getting Setup To Use Graph and Relational Databases

  30. Optional Material: A Deeper Dive Into Semantic Web and Linked Data

  31. Open Knowledge Format (OKF) for Human-Agent Systems

  32. Symbolic Math in Pure Python