NOC:Introduction to Machine Learning (Tamil)

Lecture 1 - Paradigms of Machine Learning

Lecture 2 - Few more examples

Lecture 3 - Types of Learning

Lecture 4 - Types of supervised learning

Lecture 5 - Mathematical tools

Lecture 6 - Three Fundamental spaces

Lecture 7 - Conditional Probability

Lecture 8 - Bayes Theorem

Lecture 9 - Continuous Probability

Lecture 10 - Introduction to vectors

Lecture 11 - Span of vectors

Lecture 12 - Linear Independence

Lecture 13 - Basis of vector space

Lecture 14 - Orthogonality and Projection

Lecture 15 - Introduction to Regression

Lecture 16 - Linear regression

Lecture 17 - Geometrical Interpretation

Lecture 18 - Visual Guide to Orthogonal Projection

Lecture 19 - Iterative solution: Gradient descent

Lecture 20 - Gradient Descent

Lecture 21 - Choosing Step size

Lecture 22 - Taylor Series

Lecture 23 - Stochastic Gradient Descent and basis functions

Lecture 24 - Regularization Techniques

Lecture 25 - Binary Classification

Lecture 26 - K-Nearest Neighbour Classification

Lecture 27 - Distance metric and Cross-Validation

Lecture 28 - Computational efficiency of KNN

Lecture 29 - Introduction to Decision Trees

Lecture 30 - Level splitting

Lecture 31 - Measure of Impurity

Lecture 32 - Entropy and Information Gain

Lecture 33 - Generative vs Discriminative models

Lecture 34 - Naive Bayes classifier

Lecture 35 - Conditional Independence

Lecture 36 - Classifying the test point and summary

Lecture 37 - Discriminative models

Lecture 38 - Logistic Regression

Lecture 39 - Summary and big picture

Lecture 40 - Maximum likelihood estimation

Lecture 41 - Linear separability

Lecture 42 - Perceptron and its learning algorithm

Lecture 43 - Perceptron : A thing of past

Lecture 44 - Support Vector Machine

Lecture 45 - Optimizing weights

Lecture 46 - Handling Outliers

Lecture 47 - Dual Formulation

Lecture 48 - Kernel formulation

Lecture 49 - Introduction to Ensemble methods

Lecture 50 - Bagging

Lecture 51 - Bootstrapping

Lecture 52 - Limitations of bagging

Lecture 53 - Introduction to boosting

Lecture 54 - Ada boost

Lecture 55 - Unsupervised learning

Lecture 56 - K-means Clustering

Lecture 57 - LLyod's Algorithms

Lecture 58 - Convergence and Initialization

Lecture 59 - Representation Learning

Lecture 60 - Orthogonal Projection

Lecture 61 - Covariance Matrix and Eigen direction

Lecture 62 - PCA and mean centering