Modules / Lectures
Module NameDownload

Sl.No Chapter Name English
1Lec 1: Rules of probabilityDownload
Verified
2Lec 44: Higher-dimensional data in biologyDownload
Verified
3Lec 2: Discrete probability distributionDownload
Verified
4Lec 45: Principle component analysisDownload
Verified
5Lec 3: Continuous probability distributionDownload
Verified
6Lec 46: Principle component analysis using RDownload
Verified
7Lec 4: Moments: mean and varianceDownload
Verified
8Lec 47: t-SNEDownload
Verified
9Lec 5: Moments: variance and covarianceDownload
Verified
10Lec 48: t-SNE using RDownload
Verified
11Lec 6: Bayes theorem and likelihoodDownload
Verified
12Lec 49: Diffusion mapsDownload
Verified
13Lec 7: Concept of statistical testsDownload
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14Lec 8: Vector and vector operationsDownload
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15Lec 9: Matrix and matrix operationsDownload
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16Lec 10: Determinant and Inverse of a matrixDownload
Verified
17Lec 11: Eigenvalue and eigenvectorDownload
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18Lec 12: Linear system of equationsDownload
Verified
19Lec 13: Singular value decompositionDownload
Verified
20Lec 14: Getting ready with RDownload
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21Lec 15: Algebraic and logical operations in RDownload
Verified
22Lec 16: Reading and writing dataDownload
Verified
23Lec 17: Statistics using R – descriptive statisticsDownload
Verified
24Lec 18: Statistics using R – t-test and ANOVADownload
Verified
25Lec 19: Linear algebra using RDownload
Verified
26Lec 20: Scatter plot, Line plot & Bar plotDownload
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27Lec 21: Histogram & Box plotDownload
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28Lec 22: Heatmap and Volcano plotDownload
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29Lec 23: Network visualizationDownload
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30Lec 24: Data visualization using ggplot2 - IDownload
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31Lec 25: Data visualization using ggplo2 - IIDownload
Verified
32Lec 26: CorrelationsDownload
Verified
33Lec 27: Linear regression - IDownload
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34Lec 28: Linear regression - IIDownload
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35Lec 29: Linear regression using RDownload
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36Lec 30: Multiple linear regressionDownload
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37Lec 31: Multiple linear regression using RDownload
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38Lec 32: Nonlinear regressionDownload
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39Lec 33: Nonlinear regression using RDownload
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40Lec 34: Clustering and classificationDownload
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41Lec 35: Logistic regressionDownload
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42Lec 36: Logistic regression using RDownload
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43Lec 37: Distance mesaures for clusteringDownload
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44Lec 38: k-means clusteringDownload
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45Lec 39: k-means clustering using RDownload
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46Lec 40: Hierarchical clusteringDownload
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47Lec 41: Hierarchical clustering using RDownload
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48Lec 42: Decision tree classifierDownload
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49Lec 43: Support vector machinesDownload
Verified


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