Machine Learning – Overview

This machine learning track introduces core ML principles from foundational concepts to modern applications. You'll explore supervised and unsupervised learning, model optimization, classification algorithms, evaluation metrics, and neural networks. This course is designed to be beginner friendly to help learners understand not just how to implement machine learning, but why it works—through hands-on examples, real-world intuition, and simplified explanations for the math-heavy topics involved.

Table of Contents

• Chapter 1: What is Machine Learning?: Motivation, real-world applications, comparison with traditional programming, types of ML (supervised, unsupervised, semi-supervised, reinforcement), and key terminology.
• Chapter 2: Supervised Learning Foundations: Regression vs classification, actual vs predicted values, residuals, and evaluation metrics such as MSE, MAE, and R².
• Chapter 3: Optimization and Gradient Descent: Loss functions like MSE and cross-entropy, intuition and math behind gradient descent, and variants like stochastic and mini-batch descent.
• Chapter 4: Underfitting, Overfitting, and the Bias-Variance Tradeoff: Model complexity, underfitting vs overfitting visuals, generalization, and model capacity.
• Chapter 5: Regularization Techniques: L1 (Lasso) and L2 (Ridge) regularization, how they work mathematically, and when to use each.
• Chapter 6: Model Training and Evaluation: Train/test splits, k-fold cross-validation, bootstrapping, and hyperparameter tuning.
• Chapter 7: Classification Metrics and Evaluation: Confusion matrix, accuracy, precision, recall, F1-score, and ROC-AUC curves.
• Chapter 8: Core Classification Algorithms: Logistic regression, KNN, decision trees, and random forests with intuition, equations, and tradeoffs.
• Chapter 9: Dimensionality Reduction: Curse of dimensionality, PCA, and interpreting reduced feature spaces.
• Chapter 10: Unsupervised Learning: K-Means, hierarchical clustering, elbow method, and visualization tips.
• Chapter 11: Neural Networks Basics: Perceptrons, layers, forward/backward propagation, activation functions, and training strategies.