1Introduction
10Overfitting, Underfitting, and the Bias-Variance Tradeoff
2Introduction to Machine Learning and Its Real-World Impact
11Hyperparameter Tuning and Model Optimization
3The Foundations: Data, Algorithms, and Models
12Deploying Machine Learning Models in Production
4Supervised Learning Demystified
13Tools, Libraries, and Frameworks Every ML Engineer Should Know
5Diving into Unsupervised Learning
14Ethics and Bias in Machine Learning
