1.1 Introduction to Machine Learning
1.2 What is AI?
1.3 What is Machine Learning?
1.4 What is Deep Learning?
1.5 The Role of Data Science
1.6 Data Science Techniques
Test Your Knowledge Quiz
2.1 Introduction to the 3 Pillars of Machine Learning
2.2 Supervised Learning
2.3 Unsupervised Learning
2.4 Reinforcement Learning
3.1 Introduction to Model & Instance-based Learning
3.2 Instance-based Learning
3.3 Model-based Learning
3.4 Conventional vs Instance-based Learning
4.1 Introduction to Challenges with Machine Learning
4.2 Information Collection
4.3 Insufficient Data
4.4 Non-representative Data
4.5 Data Quality & Quantity
4.6 Overfitting & Underfitting
4.7 Model Interpretability
4.8 Irrelevant Features
4.9 Bias & Fairness
5.1 Introduction to Scikit-learn
5.2 Scikit-learn's Foundation & Architecture
5.3 The Consistent API – Fit, Transform, Predict
5.4 The All-in-one Toolkit & Key Advantages
5.5 From Data to Model – The Complete Workflow
6.1 Introduction to Scikit-learn Components & Datasets
6.2 Dataset Sources & Important Considerations
6.3 Working with Toy Datasets - Iris Example
6.4 Working with Realistic Datasets
6.5 Loading Custom Datasets & ML Workflow Overview
