| Topic | Slides | Video |
|---|---|---|
| Topic 1.1: Core Foundations
Introduction to Machine Learning: Definition, types of learning, applications, and real-world examples |
Slides | - |
| Topic 1.2: Core Foundations
Mathematical Prerequisites: Linear algebra review, probability and statistics, calculus basics |
Slides | - |
| Topic 1.3: Core Foundations
Python Programming for ML: NumPy, Pandas, Matplotlib, Scikit-learn introduction |
Slides | - |
| Topic 1.4: Core Foundations
Data Preprocessing: Data cleaning, handling missing values, feature scaling, encoding categorical variables |
Slides | - |
| Topic 1.5: Core Foundations
Types of Machine Learning: Supervised, unsupervised, and reinforcement learning overview |
Slides | - |
| Topic 2.1: Supervised Learning
Linear Regression: Simple and multiple regression, cost functions, gradient descent |
Slides | - |
| Topic 2.2: Supervised Learning
Logistic Regression: Binary and multiclass classification, sigmoid function, maximum likelihood |
Slides | - |
| Topic 2.3: Supervised Learning
Decision Trees: Construction, pruning, entropy, information gain, Gini impurity |
Slides | - |
| Topic 2.4: Supervised Learning
Ensemble Methods: Random forests, bagging, boosting (AdaBoost, Gradient Boosting) |
Slides | - |
| Topic 2.5: Supervised Learning
Support Vector Machines: Linear and non-linear SVM, kernel trick, margin optimization |
Slides | - |
| Topic 2.6: Supervised Learning
k-Nearest Neighbors: Distance metrics, curse of dimensionality, choosing k |
Slides | - |
| Topic 2.7: Supervised Learning
Naive Bayes: Gaussian, multinomial, and Bernoulli variants |
Slides | - |
| Topic 3.1: Model Evaluation and Selection
Performance Metrics: Accuracy, precision, recall, F1-score, ROC curves, AUC |
Slides | - |
| Topic 3.2: Model Evaluation and Selection
Cross-Validation: k-fold, leave-one-out, stratified sampling |
Slides | - |
| Topic 3.3: Model Evaluation and Selection
Bias-Variance Tradeoff: Overfitting, underfitting, model complexity |
Slides | - |
| Topic 3.4: Model Evaluation and Selection
Hyperparameter Tuning: Grid search, random search, validation curves |
Slides | - |
| Topic 4.1: Unsupervised Learning
Clustering: k-means, hierarchical clustering, DBSCAN, evaluation metrics |
Slides | - |
| Topic 4.2: Unsupervised Learning
Dimensionality Reduction: Principal Component Analysis (PCA), t-SNE basics |
Slides | - |
| Topic 4.3: Unsupervised Learning
Association Rules: Market basket analysis, Apriori algorithm |
Slides | - |
| Topic 5.1: Advanced Topics
Neural Networks Introduction: Perceptron, multi-layer perceptrons, backpropagation |
Slides | - |
| Topic 5.2: Advanced Topics
Deep Learning Basics: Introduction to deep networks, activation functions, common architectures |
Slides | - |
| Topic 5.3: Advanced Topics
Feature Engineering: Feature selection, creation, and transformation techniques |
Slides | - |
| Topic 5.4: Advanced Topics
Time Series Analysis: Basic forecasting, trend analysis, seasonality |
Slides | - |
| Topic 5.5: Advanced Topics
Ethics in ML: Bias, fairness, interpretability, responsible AI practices |
Slides | - |