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1. Foundations (Prerequisites and Introduction) Introduction to Machine Learning
What is ML? (Lecture 1 )
Supervised vs. Unsupervised vs. Reinforcement Learning (Lecture 2 )
Applications of Machine Learning (Activity 1 )
Basic ML Terminology (Lecture 3 )
Python for Machine Learning
NumPy for numerical operations (Numpy essentials )
Pandas for data manipulation (Pandas essentials )
Matplotlib/Seaborn for data visualization (Matplotlib essentials )
Scikit-learn basics for loading datasets, splitting data (Scikit-learn essentials )
Overview of Mathematical Foundations
Linear Algebra (Importance of LA in ML )
Probability & Statistics (Importance of Probability & Statistics in ML )
Calculus (Importance of Calculus in ML )
2. Supervised Learning - Regression Data Preprocessing and Exploration
Handling missing values (Reading from geeks-for-geeks ]
Feature scaling - standardization, normalization - (Reading from geeks-for-geeks )
One-hot encoding for categorical features (Reading from geeks-for-geeks )
Exploratory Data Analysis EDA (Reading from geeks-for-geeks )
Simple Linear Regression (Video: Mathematical intuition )
Cost function (Mean Squared Error - MSE)
Gradient Descent algorithm (Video: Mathematical intuition )
Multiple Linear Regression (Reading from Geeks-for-geeks )
Assumptions of Linear Regression (Reading from Geeks-for-geeks )
Implementation (Coding step-by-step )
Lab Assignment - 1 (Lab Activity )
Model Evaluation for Regression
R^2, Adjusted R^2 (Videos: Intuition behind R^2 , Intuition behind Adjusted R^2 )
MAE, MSE, RMSE
Overfitting and Underfitting (Intuition )
Bias-Variance Tradeoff (Intuition behind )
Multi-colinearity (Reading from Geeks-for-geeks )
Cross-validation (k-fold, leave-one-out) (Intuition )
Ridge Regression (L2 regularization) (Intuition , Reading )
Lasso Regression (L1 regularization) (Intuition , Reading )
Elastic Net (Intuition )
Lab Assignment - 2 (Lab Activity )
3. Supervised Learning - Classification Introduction to Classification
Regression vs. Classification (Reading )
False Positives (Type I error) vs. False Negatives (Type II error) (Reading )
Classification metrics (Accuracy, Precision, Recall, F1-score, Confusion Matrix) (Reading )
ROC Curve and AUC (Reading ) (Class Notes )
Binary vs. Multi-class classification
Why not linear regression for classification? (Reading )
Sigmoid function
Cost function (Binary Cross-Entropy)
Class notes Decision boundary
Implementation (Coding Logistic Regression )
Lab Assignment - 3 (Lab Activity )
K-Nearest Neighbors (KNN)
Distance metrics
Choosing 'k'
Reference (Reading )
Bayes' Theorem
Naive Bayes classifier (Reading )
Feature independence
Discrete vs. continuous features
Lab Assignment - 4 (Lab Activity )
Support Vector Machines (SVM)
Linear SVM (maximal margin hyperplane)
Class notes Kernels (polynomial, RBF) for non-linear separation
Mathematical Derivation (Readings: Part 1 , Part 2 , Part 3 )
Lab Assignment - 5 (Lab Activity )
Entropy, Gini impurity
Information Gain
Pruning
Lab Assignment - 6 (Lab Activity )
Bagging: Random Forest
Boosting: AdaBoost (Video )
K-Means Clustering: Elbow method, silhouette score
Hierarchical Clustering: Dendrograms
DBSCAN (briefly).
Lab Assignment -7 (Lab Activity )
Principal Component Analysis (PCA): Intuition, eigen decomposition (briefly), applications
PCA computation (Class Notes )
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