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