Machine Learning
This post mainly contains major machine learning, deep learning algorithms.
Prerequisites - Linear Algebra, Statistics, Learn Linear Algebra
(Lecture 1) Introduction to ML for overview.
(Lecture 2) ML Workflow for Data Representations, Data Transformations, Data Visualisation
(Lecture 3) Performance Measures MutiClass, BinaryClass Classification
(Lecture 4) K-Nearest Neighbours Most basic & introductory algorithm in machine-learning
(Lecture 5) Decision Tree Most intuitive algorithm in machine-learning
(Lecture 6) Support Vector Machine one of the oldest machine learning algorithm
(Lecture 7-part1) K-Means Clustering Unsupervised Machine Learning Algorithm
(Lecture 7-part2) K-Means Clustering Unsupervised Machine Learning Algorithm
(Lecture 9) Principal Component Analysis One of the dimensionality reduction technique
(Lecture 10) Logistic Regression Binary classification algorithm, can be used for multi-class classification
(Lecture 11) Neural Network CheckList
(Lecture 12) Multi Layer Perceptron Introduction to Neural Networks
(Lecture 14) GMM(part1)Gaussian Mixture Model and Hirerchichal clustering
(Lecture 15) GMM(part2)Gaussian Mixture Model and Hirerchichal clustering
(Lecture 16) CNN(Convolution Neural Network)
(Lecture 17) Bias Variance trade-off
(Lecture 18) Non-Parametric Density Estimation
(Lecture 19-part1) ML for sequential data
(Lecture 19-part2) ML for sequential data