Introduction to Machine Learning (Tamil)

NPTEL-NOC IITM via YouTube

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Learn the fundamental concepts and algorithms of machine learning through this comprehensive course delivered in Tamil by NPTEL-NOC IITM. Master essential mathematical foundations including probability theory, Bayes theorem, linear algebra concepts like vectors, orthogonality, and projections. Explore supervised learning techniques such as linear regression with gradient descent optimization, classification methods including K-nearest neighbors, decision trees with entropy and information gain, naive Bayes classifier, logistic regression, perceptron algorithm, and support vector machines with kernel formulations. Understand ensemble methods like bagging, bootstrapping, and AdaBoost for improved model performance. Dive into unsupervised learning through K-means clustering with Lloyd's algorithm and principal component analysis for dimensionality reduction. Gain practical experience with Python programming tutorials covering numpy, gradient descent implementation, logistic regression, perceptron, and PCA. Develop a solid foundation in machine learning paradigms, mathematical tools, and algorithmic approaches while learning to handle real-world challenges like overfitting through regularization techniques and outlier management in support vector machines.

Syllabus

#1 Overview | Introduction to Machine Learning (Tamil)
#2 Paradigms of Machine Learning | Introduction to Machine Learning (Tamil) 1.1
#3 Few more examples | Introduction to Machine Learning (Tamil) 1.2
#4 Types of Learning | Introduction to Machine Learning (Tamil) 1.3
#5 Types of supervised learning | Introduction to Machine Learning (Tamil) 1.4
#6 Mathematical tools | Introduction to Machine Learning (Tamil) 1.5
#7 Three Fundamental spaces | Introduction to Machine Learning (Tamil) 1.6
#8 Conditional Probability | Introduction to Machine Learning (Tamil) 1.7
#9 Bayes Theorem | Introduction to Machine Learning (Tamil) 1.8
#10 Continuous Probability | Introduction to Machine Learning (Tamil) 1.9
#11 Introduction to vectors | Introduction to Machine Learning (Tamil) 2.1
#12 Span of vectors | Introduction to Machine Learning (Tamil) 2.2
#13 Linear Independence | Introduction to Machine Learning (Tamil) 2.3
#14 Basis of vector space | Introduction to Machine Learning (Tamil) 2.4
#15 Orthogonality and Projection | Introduction to Machine Learning (Tamil) 2.5
#16 Introduction to Regression | Introduction to Machine Learning (Tamil) 2.6
#17 Linear regression | Introduction to Machine Learning (Tamil) 2.7
#18 Geometrical Interpretation | Introduction to Machine Learning (Tamil) 2.8
#19 Visual Guide to Orthogonal Projection | Introduction to Machine Learning (Tamil) 2.9
#20 Iterative solution: Gradient descent | Introduction to Machine Learning (Tamil) 3.1
#21 Gradient Descent | Introduction to Machine Learning (Tamil) 3.2
#22 Choosing Step size | Introduction to Machine Learning (Tamil) 3.3
#23 Taylor Series | Introduction to Machine Learning (Tamil) 3.4
#24 Stochastic Gradient Descent and basis Functions | Introduction to Machine Learning (Tamil) 3.5
#25 Regularization Techniques | Introduction to Machine Learning (Tamil) 3.6
#26 Binary Classification | Introduction to Machine Learning (Tamil) 4.1
#27 K-Nearest Neighbour Classification | Introduction to Machine Learning (Tamil) 4.2
#28 Distance Metric and Cross-Validation | Introduction to Machine Learning (Tamil) 4.3
#29 Computational efficiency of KNN | Introduction to Machine Learning (Tamil) 4.4
#30 Introduction to Decision Trees | Introduction to Machine Learning (Tamil) 4.5
#31 Level splitting | Introduction to Machine Learning (Tamil) 4.6
#32 Measure of Impurity | Introduction to Machine Learning (Tamil) 4.7
#33 Entropy and Information Gain | Introduction to Machine Learning (Tamil) 4.8
#34 Generative vs Discriminative models | Introduction to Machine Learning (Tamil) 4.9
#35 Naive Bayes classifier | Introduction to Machine Learning (Tamil) 4.10
#36 Conditional Independence | Introduction to Machine Learning (Tamil) 4.11
#37 Classifying the Test Point and Summary | Introduction to Machine Learning (Tamil) 4.12
#38 Discriminative models | Introduction to Machine Learning (Tamil) 5.1
#39 Logistic Regression | Introduction to Machine Learning (Tamil) 5.2
#40 Summary and big picture | Introduction to Machine Learning (Tamil) 5.3
#41 Maximum likelihood Estimation | Introduction to Machine Learning (Tamil) 5.4
#42 Linear Separability | Introduction to Machine Learning (Tamil) 5.5
#43 Perceptron and its learning Algorithm | Introduction to Machine Learning (Tamil) 5.6
#44 Perceptron : A thing of Past | Introduction to Machine Learning (Tamil) 5.7
#45 Support Vector Machine | Introduction to Machine Learning (Tamil) 6.1
#46 Optimizing weights | Introduction to Machine Learning (Tamil) 6.2
#47 Handling Outliers | Introduction to Machine Learning (Tamil) 6.3
#48 Dual Formulation | Introduction to Machine Learning (Tamil) 6.4
#49 Kernel formulation | Introduction to Machine Learning (Tamil) 6.5
#50 Introduction to Ensemble methods | Introduction to Machine Learning (Tamil) 7.1
#51 Bagging | Introduction to Machine Learning (Tamil) 7.2
#52 Bootstrapping | Introduction to Machine Learning (Tamil) 7.3
#53 Limitations of bagging | Introduction to Machine Learning (Tamil) 7.4
#54 Introduction to boosting | Introduction to Machine Learning (Tamil) 7.5
#55 Ada boost | Introduction to Machine Learning (Tamil) 7.6
#56 Unsupervised learning | Introduction to Machine Learning (Tamil) 8.1
#57 K-means Clustering | Introduction to Machine Learning (Tamil) 8.2
#58 LLyod's Algorithms | Introduction to Machine Learning (Tamil) 8.3
#59 Convergence and Initialization | Introduction to Machine Learning (Tamil) 8.4
#60 Representation Learning | Introduction to Machine Learning (Tamil) 8.5
#61 Orthogonal Projection | Introduction to Machine Learning (Tamil) 8.6
#62 Covariance Matrix and Eigen direction | Introduction to Machine Learning (Tamil) 8.7
#63 PCA and mean centering | Introduction to Machine Learning (Tamil) 8.8
#64 Concluding remarks | Introduction to Machine Learning (Tamil) 4.10
W4-Tutorial-1-KNN
W4-Tutorial-2-Decision-Tree
W4-Tutorial-3-Naive-Bayes
Basics of Python
Basics of numpy
Gradient Descent using python
Logistic Regression Tutorial
Perceptron Tutorial
Principal Component Analysis - Tutorial