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Sl.No Chapter Name MP4 Download
1A brief introduction to machine learningDownload
2Supervised LearningDownload
3Unsupervised LearningDownload
4Reinforcement LearningDownload
5Probability Basics - 1Download
6Probability Basics - 2Download
7Linear Algebra - 1Download
8Linear Algebra - 2Download
9Statistical Decision Theory - RegressionDownload
10Statistical Decision Theory - ClassificationDownload
11Bias-VarianceDownload
12Linear RegressionDownload
13Multivariate RegressionDownload
14Subset Selection 1Download
15Subset Selection 2Download
16Shrinkage MethodsDownload
17Principal Components RegressionDownload
18Partial Least SquaresDownload
19Linear ClassificationDownload
20Logistic RegressionDownload
21Linear Discriminant Analysis 1Download
22Linear Discriminant Analysis 2Download
23Linear Discriminant Analysis 3Download
24OptimizationDownload
25Perceptron LearningDownload
26SVM - FormulationDownload
27SVM - Interpretation & AnalysisDownload
28SVMs for Linearly Non Separable DataDownload
29SVM KernelsDownload
30SVM - Hinge Loss FormulationDownload
31Weka TutorialDownload
32Early ModelsDownload
33Backpropogation IDownload
34Backpropogation IIDownload
35Initialization, Training & ValidationDownload
36Maximum Likelihood EstimateDownload
37Priors & MAP EstimateDownload
38Bayesian Parameter Estimation Download
39IntroductionDownload
40Regression TreesDownload
41Stopping Criteria & PruningDownload
42Loss Functions for ClassificationDownload
43Categorical AttributesDownload
44Multiway SplitsDownload
45Missing Values, Imputation & Surrogate SplitsDownload
46Instability, Smoothness & Repeated SubtreesDownload
47TutorialDownload
48Evaluation Measures IDownload
49Bootstrapping & Cross ValidationDownload
502 Class Evaluation MeasuresDownload
51The ROC CurveDownload
52Minimum Description Length & Exploratory AnalysisDownload
53Introduction to Hypothesis TestingDownload
54Basic ConceptsDownload
55Sampling Distributions & the Z TestDownload
56Student\'s t-testDownload
57The Two Sample & Paired Sample t-testsDownload
58Confidence Intervals Download
59Bagging, Committee Machines & StackingDownload
60BoostingDownload
61Gradient BoostingDownload
62Random ForestDownload
63Naive Bayes Download
64Bayesian NetworksDownload
65Undirected Graphical Models - IntroductionDownload
66Undirected Graphical Models - Potential FunctionsDownload
67Hidden Markov ModelsDownload
68Variable EliminationDownload
69Belief PropagationDownload
70Partitional ClusteringDownload
71Hierarchical ClusteringDownload
72Threshold GraphsDownload
73The BIRCH AlgorithmDownload
74The CURE AlgorithmDownload
75Density Based ClusteringDownload
76Gaussian Mixture ModelsDownload
77Expectation MaximizationDownload
78Expectation Maximization ContinuedDownload
79Spectral ClusteringDownload
80Learning TheoryDownload
81Frequent Itemset MiningDownload
82The Apriori PropertyDownload
83Introduction to Reinforcement LearningDownload
84RL Framework and TD LearningDownload
85Solution Methods & ApplicationsDownload
86Multi-class ClassificationDownload

Sl.No Chapter Name English
1A brief introduction to machine learningDownload
Verified
2Supervised LearningDownload
Verified
3Unsupervised LearningDownload
Verified
4Reinforcement LearningDownload
Verified
5Probability Basics - 1Download
Verified
6Probability Basics - 2Download
Verified
7Linear Algebra - 1Download
Verified
8Linear Algebra - 2Download
Verified
9Statistical Decision Theory - RegressionDownload
Verified
10Statistical Decision Theory - ClassificationDownload
Verified
11Bias-VarianceDownload
Verified
12Linear RegressionDownload
Verified
13Multivariate RegressionDownload
Verified
14Subset Selection 1Download
Verified
15Subset Selection 2Download
Verified
16Shrinkage MethodsDownload
Verified
17Principal Components RegressionDownload
Verified
18Partial Least SquaresDownload
Verified
19Linear ClassificationDownload
Verified
20Logistic RegressionDownload
Verified
21Linear Discriminant Analysis 1Download
Verified
22Linear Discriminant Analysis 2Download
Verified
23Linear Discriminant Analysis 3Download
Verified
24OptimizationDownload
Verified
25Perceptron LearningDownload
Verified
26SVM - FormulationDownload
Verified
27SVM - Interpretation & AnalysisDownload
Verified
28SVMs for Linearly Non Separable DataDownload
Verified
29SVM KernelsDownload
Verified
30SVM - Hinge Loss FormulationDownload
Verified
31Weka TutorialDownload
Verified
32Early ModelsDownload
Verified
33Backpropogation IDownload
Verified
34Backpropogation IIDownload
Verified
35Initialization, Training & ValidationDownload
Verified
36Maximum Likelihood EstimateDownload
Verified
37Priors & MAP EstimateDownload
Verified
38Bayesian Parameter Estimation Download
Verified
39IntroductionDownload
Verified
40Regression TreesDownload
Verified
41Stopping Criteria & PruningDownload
Verified
42Loss Functions for ClassificationDownload
Verified
43Categorical AttributesDownload
Verified
44Multiway SplitsDownload
Verified
45Missing Values, Imputation & Surrogate SplitsDownload
Verified
46Instability, Smoothness & Repeated SubtreesDownload
Verified
47TutorialDownload
Verified
48Evaluation Measures IDownload
Verified
49Bootstrapping & Cross ValidationDownload
Verified
502 Class Evaluation MeasuresDownload
Verified
51The ROC CurveDownload
Verified
52Minimum Description Length & Exploratory AnalysisDownload
Verified
53Introduction to Hypothesis TestingDownload
Verified
54Basic ConceptsDownload
Verified
55Sampling Distributions & the Z TestDownload
Verified
56Student\'s t-testDownload
Verified
57The Two Sample & Paired Sample t-testsDownload
Verified
58Confidence Intervals Download
Verified
59Bagging, Committee Machines & StackingDownload
Verified
60BoostingDownload
Verified
61Gradient BoostingDownload
Verified
62Random ForestDownload
Verified
63Naive Bayes Download
Verified
64Bayesian NetworksDownload
Verified
65Undirected Graphical Models - IntroductionDownload
Verified
66Undirected Graphical Models - Potential FunctionsDownload
Verified
67Hidden Markov ModelsDownload
Verified
68Variable EliminationDownload
Verified
69Belief PropagationDownload
Verified
70Partitional ClusteringDownload
Verified
71Hierarchical ClusteringDownload
Verified
72Threshold GraphsDownload
Verified
73The BIRCH AlgorithmDownload
Verified
74The CURE AlgorithmDownload
Verified
75Density Based ClusteringDownload
Verified
76Gaussian Mixture ModelsDownload
Verified
77Expectation MaximizationDownload
Verified
78Expectation Maximization ContinuedDownload
Verified
79Spectral ClusteringDownload
Verified
80Learning TheoryDownload
Verified
81Frequent Itemset MiningDownload
Verified
82The Apriori PropertyDownload
Verified
83Introduction to Reinforcement LearningDownload
Verified
84RL Framework and TD LearningDownload
Verified
85Solution Methods & ApplicationsDownload
Verified
86Multi-class ClassificationDownload
Verified


Sl.No Language Book link
1EnglishNot Available
2BengaliNot Available
3GujaratiNot Available
4HindiNot Available
5KannadaNot Available
6MalayalamNot Available
7MarathiNot Available
8TamilNot Available
9TeluguNot Available