Module 4: Machine Learning & Deep Learning Foundations#
CodeVision Python Training
Contents#
Group 1: The Big Picture (Sections 4.1-4.5)
Group 2: Anatomy of a Neural Network (Sections 4.6-4.10)
Group 3: How Models Learn (Sections 4.11-4.15)
Group 4: Reality, Frameworks, and Why This Matters (Sections 4.16-4.20)
Welcome to Module 4#
This module explains how machine learning and deep learning actually work, and why modern AI systems (including LLMs) behave the way they do.
It is conceptual first, with light, real code to ground ideas. By the end of this module, the behaviour of LLMs should feel inevitable, not mysterious.
This module builds directly on:
Module 1: Python fundamentals (functions, JSON, notebooks)
Module 2: Data work with Pandas and visualisation
Module 3: LLM Fundamentals (inference, hallucinations, constraints)
What You Will Learn#
Topic |
Why It Matters |
|---|---|
AI vs ML vs DL vs GenAI |
Set correct expectations and vocabulary |
Neural network anatomy |
Understand what models actually are |
Weights, biases, activations |
Grasp the building blocks |
Loss functions & gradient descent |
Know how models learn |
Overfitting vs underfitting |
Recognise and mitigate common failures |
PyTorch vs TensorFlow/Keras |
Understand the two dominant frameworks |
Why LLMs hallucinate |
Connect training dynamics to behaviour |
Why grounding (RAG) is required |
Bridge to enterprise reliability |
Prerequisites#
Before starting this module, ensure you have:
Completed Module 1 (Python Foundations)
Completed Module 2 (Python for Data Work)
Completed Module 3 (LLM Fundamentals)
Module 4 Learning Path#
Content - Work through the interactive notebook
Quiz - Test your understanding (auto-graded)
Assessment - Written explanations demonstrating comprehension (auto-graded)
Resources - Additional learning materials
End of Module 4 Introduction#
Click Content in the navigation to begin the interactive lesson.