Module 1: Basics
Module1_Lab1: Python Basics
Module1_Lab2: Data and Features
Module1_Lab3: Terms and Metrics
Module1_Lab4: Linear Algebra
Module 2: Appreciating, Interpreting and Visualizing Data
Module2_Lab1: Basic Plots - Data Visualization
Module2_Lab2: Principal Component Analysis (PCA) - Dimensionality Reduction
Module2_Lab3: Manifold Learning Methods - Dimensionality Reduction
Module2_Lab4: t-Distributed Stochastic Neighbor Embedding (t-SNE) - Dimensionality Reduction
Module2_Project: Appreciating, Interpreting and Visualizing Data
Module 3: Classification
Module3_Lab1: Understanding Distance metrics and Introduction to KNN
Module3_Lab2: Implementing KNN from scratch and visualize Algorithm performance
Module3_Lab3: Using KNN for Text Classification
Module3_Lab4: Understanding Cross-Validation and Standardization
Module3_Project: Data Visualization, Choosing K-value and Appreciating Feature Scaling and Standardization
Module 4: Perceptron and Gradient Descent & SVM
Module4_Lab1: Perceptron
Module4_Lab2: Introduction to Gradient Descent
Module4_Lab3: Gradient Descent
Module4_Lab4: Support Vector Machines
Module4_Project: Perceptron and Gradient Descent
Module 5: SVM & Classification
Module5_Lab1: Support Vector Machines
Module5_Lab2: Introduction to Decision Trees
Module5_Lab3: Information Metrics and generalizability of Decision Trees
Module5_Lab4: Ensemble Methods and Random Forests
Module5_Project: ML based Finger Counter
Module 6: Regression
Module6_Lab1: Linear Regression, MSE and Polynomial Regression
Module6_Lab2: Loss Functions
Module6_Lab3: Regularization and Logistic Regression
Module6_Project: Regression analysis on a COVID-dataset
Module 7: Clustering
Module7_Lab1: K-Means
Module7_Lab2: Hierarchical Clustering
Module7_Lab3: Matrix Factorization (Based on Recommender System Example)
Module7_Lab4: Anomaly/Outlier Detection
Module7_Project: Movie Recommendation Engine
Module 8: Bayesian Machine Learning
Module8_Lab1: Introduction to Probability Theory
Module8_Lab2: Naive Bayes Classifiers
Module8_Lab3: Applying Bayes Classifiers
Module8_Lab4: Probabilisitic Mixture Models
Module 9: Neural Networks
Module9_Lab1: Introduction to Multi-Layer Perceptron (MLP)
Module9_Lab2: Using MLP for multiclass classification
Module9_Lab3: Convolutional Neural Networks
Module9_Lab4: CNN - Using learnt representations
Module9_Project1: Breast cancer prediction with an MLP Classifier
Module9_Project2: Emotion recognition using CNNs