Modules / Lectures


New Assignments
Module NameDownload
noc17-mg24_Week_01_Assignment_01noc17-mg24_Week_01_Assignment_01
noc17-mg24_Week_02_Assignment_01noc17-mg24_Week_02_Assignment_01
noc17-mg24_Week_03_Assignment_01noc17-mg24_Week_03_Assignment_01
noc17-mg24_Week_04_Assignment_01noc17-mg24_Week_04_Assignment_01
noc17-mg24_Week_05_Assignment_01noc17-mg24_Week_05_Assignment_01
noc17-mg24_Week_06_Assignment_01noc17-mg24_Week_06_Assignment_01
noc17-mg24_Week_07_Assignment_01noc17-mg24_Week_07_Assignment_01
noc17-mg24_Week_08_Assignment_01noc17-mg24_Week_08_Assignment_01


Sl.No Chapter Name MP4 Download
1Course OverviewDownload
2Course Overview (cont\'d)Download
3Descriptive Statistics - Graphical ApproachesDownload
4Descriptive Statistics - Measures of Central TendencyDownload
5Descriptive Statistics - Measures of DispersionDownload
6Random Variables and Probability DistributionsDownload
7Probability Distributions(cont\'d)Download
8Probability Distributions(cont\'d)Download
9Inferential Statistics - MotivationDownload
10Inferential Statistics - Single sample testsDownload
11Two Sample testsDownload
12Type 1 and Type 2 ErrorsDownload
13Confidence IntervalsDownload
14ANOVA and Test of IndependenceDownload
15Short Introduction to RegressionDownload
16Introduction to Machine LearningDownload
17Supervised LearningDownload
18Unsupervised LearningDownload
19Ordinary Least Squares RegressionDownload
20Simple and Multiple Regression in Excel and MatlabDownload
21Regularization/ Coefficients ShrinkageDownload
22Data Modelling and Algorithmic Modelling ApproachesDownload
23Logistic RegressionDownload
24Training a Logistic Regression ClassifierDownload
25Classification and Regression TreesDownload
26Classification and Regression Trees(cont\'d)Download
27Bias Variance DichotomyDownload
28Model Assessment and SelectionDownload
29Support Vector MachinesDownload
30Support Vector Machines(cont\'d)Download
31Support Vector Machines for Non Linearly Separable DataDownload
32Support Vector Machines and Kernel TransformationsDownload
33Ensemble Methods and Random ForestsDownload
34Artificial Neural NetworksDownload
35Artificial Neural Networks(cont\'d)Download
36Deep LearningDownload
37Associative Rule MiningDownload
38Association Rule Mining (cont\'d)Download
39Big Data, A small introductionDownload
40Big Data - A small introduction (cont\'d)Download
41Clustering AnalysisDownload
42Clustering Analysis (cont\'d)Download
43Introduction to Experimentation and Active LearningDownload
44Introduction to Experimentation and Active Learning(cont\'d)Download
45An Introduction to Online Learning - Reinforcement LearningDownload
46An Introduction to Online Learning - Reinforcement Learning (cont\'d)Download
47Summary+ Insights into the Final ExamDownload
48Tutorial on WekaDownload
49Tutorial on Decision TreesDownload

Sl.No Chapter Name English
1Course OverviewPDF unavailable
2Course Overview (cont\'d)PDF unavailable
3Descriptive Statistics - Graphical ApproachesPDF unavailable
4Descriptive Statistics - Measures of Central TendencyPDF unavailable
5Descriptive Statistics - Measures of DispersionPDF unavailable
6Random Variables and Probability DistributionsPDF unavailable
7Probability Distributions(cont\'d)PDF unavailable
8Probability Distributions(cont\'d)PDF unavailable
9Inferential Statistics - MotivationPDF unavailable
10Inferential Statistics - Single sample testsPDF unavailable
11Two Sample testsPDF unavailable
12Type 1 and Type 2 ErrorsPDF unavailable
13Confidence IntervalsPDF unavailable
14ANOVA and Test of IndependencePDF unavailable
15Short Introduction to RegressionPDF unavailable
16Introduction to Machine LearningPDF unavailable
17Supervised LearningPDF unavailable
18Unsupervised LearningPDF unavailable
19Ordinary Least Squares RegressionPDF unavailable
20Simple and Multiple Regression in Excel and MatlabPDF unavailable
21Regularization/ Coefficients ShrinkagePDF unavailable
22Data Modelling and Algorithmic Modelling ApproachesPDF unavailable
23Logistic RegressionPDF unavailable
24Training a Logistic Regression ClassifierPDF unavailable
25Classification and Regression TreesPDF unavailable
26Classification and Regression Trees(cont\'d)PDF unavailable
27Bias Variance DichotomyPDF unavailable
28Model Assessment and SelectionPDF unavailable
29Support Vector MachinesPDF unavailable
30Support Vector Machines(cont\'d)PDF unavailable
31Support Vector Machines for Non Linearly Separable DataPDF unavailable
32Support Vector Machines and Kernel TransformationsPDF unavailable
33Ensemble Methods and Random ForestsPDF unavailable
34Artificial Neural NetworksPDF unavailable
35Artificial Neural Networks(cont\'d)PDF unavailable
36Deep LearningPDF unavailable
37Associative Rule MiningPDF unavailable
38Association Rule Mining (cont\'d)PDF unavailable
39Big Data, A small introductionPDF unavailable
40Big Data - A small introduction (cont\'d)PDF unavailable
41Clustering AnalysisPDF unavailable
42Clustering Analysis (cont\'d)PDF unavailable
43Introduction to Experimentation and Active LearningPDF unavailable
44Introduction to Experimentation and Active Learning(cont\'d)PDF unavailable
45An Introduction to Online Learning - Reinforcement LearningPDF unavailable
46An Introduction to Online Learning - Reinforcement Learning (cont\'d)PDF unavailable
47Summary+ Insights into the Final ExamPDF unavailable
48Tutorial on WekaPDF unavailable
49Tutorial on Decision TreesPDF unavailable


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