Lecture 1 - Introduction to Machine Learning
Lecture 2 - Linear Algebra: Review (Vector Spaces)
Lecture 3 - Linear Algebra: Review (Matrices)
Lecture 4 - Probability Theory: Review (Basics of Probability)
Lecture 5 - Probability Theory: Review (Random Variables)
Lecture 6 - Linear Regression
Lecture 7 - Linear Regression
Lecture 8 - Tutorial: Linear Regression
Lecture 9 - Linear Regression
Lecture 10 - Linear Kernel Regression
Lecture 11 - k-Nearest Neighbour (k-NN) Regression
Lecture 12 - Tutorial: k-NN Regression
Lecture 13 - Tutorial: Kernel Regression
Lecture 14 - Logistic Regression: Classification Evaluation Metrics
Lecture 15 - Logistic Regression
Lecture 16 - Logistic Regression: Examples
Lecture 17 - Tutorial: Logistic Regression
Lecture 18 - Neural Networks
Lecture 19 - Neural Networks
Lecture 20 - Neural Networks: Examples
Lecture 21 - Tutorial: Neural Networks
Lecture 22 - Practical Machine Learning - Part 1
Lecture 23 - Practical Machine Learning - Part 2
Lecture 24 - Practical Machine Learning - Part 3
Lecture 25 - Practical Machine Learning - Part 4
Lecture 26 - Support Vector Machines (SVM)
Lecture 27 - Tutorial: Support Vector Machines (SVM)
Lecture 28 - Kernel Support Vector Machines (k-SVM)
Lecture 29 - Naïve Bayes Classification
Lecture 30 - Decision Trees - Part 1
Lecture 31 - Decision Trees - Part 2
Lecture 32 - Tutorial: Naive Bayes Classification
Lecture 33 - Tutorial: Decision Trees
Lecture 34 - k-NN Classifier
Lecture 35 - Ensemble Learning
Lecture 36 - Random Forests
Lecture 37 - Bagging (Bootstrap AGGregatING)
Lecture 38 - Tutorial: Random Forests
Lecture 39 - Tutorial: k-NN Classifier and Bootstrap AGGregatING (Bagging)
Lecture 40 - Boosting
Lecture 41 - Clustering
Lecture 42 - k-means Clustering
Lecture 43 - Tutorial: Boosting
Lecture 44 - Spectral Clustering
Lecture 45 - Mixture of Models (Gaussian Mixture Models-GMM)
Lecture 46 - Dimensionality Reduction: Principal Component Analysis (PCA) and kernel PCA
Lecture 47 - Tutorial: k-means and Spectral Clustering
Lecture 48 - Tutorial: Principal Component Analysis (PCA) and Gaussian Mixture Models (GMM)
Lecture 49 - Introduction to Deep Learning (DL)
Lecture 50 - Convolutional Neural Networks (CNN) - Part A
Lecture 51 - Convolutional Neural Networks (CNN) - Part B
Lecture 52 - Autoencoders
Lecture 53 - Applications of ML in Healthcare Problems - Part 1
Lecture 54 - Applications of ML in Healthcare Problems - Part 2
Lecture 55 - Tutorial: CNN and Autoencoder
