ACM Summer School in Data Science (USB)

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SKU
128106002



Media Storage Type : 64 GB USB Stick

NPTEL Subject Matter Expert : Prof. Murugaiyan Amirthalingam

NPTEL Co-ordinating Institute : IIT Madras

NPTEL Lecture Count : 73

NPTEL Course Size : 47 GB

NPTEL PDF Text Transcription : Not Available

NPTEL Subtitle Transcription : Not Available


Lecture Titles:

Lecture 1 - Probability - Part 1
Lecture 2 - Probability - Part 2
Lecture 3 - Probability - Part 3
Lecture 4 - Math Foundation - Part 1
Lecture 5 - Math Foundation - Part 2
Lecture 6 - Math Foundation - Part 3
Lecture 7 - Math Foundation 2 - Part 1
Lecture 8 - Math Foundation 2 - Part 2
Lecture 9 - Math Foundation 2 - Part 3
Lecture 10 - Introduction to probability for Data science - Part 1
Lecture 11 - Introduction to probability for Data science - Part 2
Lecture 12 - Introduction to probability for Data science - Part 3
Lecture 13 - Introduction to Statistics for Data science - Part 1
Lecture 14 - Introduction to Statistics for Data science - Part 2
Lecture 15 - Introduction to Statistics for Data science - Part 3
Lecture 16 - Clustering I - Part 1
Lecture 17 - Clustering I - Part 2
Lecture 18 - Clustering I - Part 3
Lecture 19 - Clustering II - Part 1
Lecture 20 - Clustering II - Part 2
Lecture 21 - Clustering II - Part 3
Lecture 22 - Dimensionality Reduction - Part 1
Lecture 23 - Dimensionality Reduction - Part 2
Lecture 24 - Dimensionality Reduction - Part 3
Lecture 25 - Supervised Learning I - Part 1
Lecture 26 - Supervised Learning I - Part 2
Lecture 27 - Supervised Learning I - Part 3
Lecture 28 - Supervised Learning II - Part 1
Lecture 29 - Supervised Learning II - Part 2
Lecture 30 - Supervised Learning II - Part 3
Lecture 31 - Supervised Learning III - Part 1
Lecture 32 - Supervised Learning III - Part 2
Lecture 33 - Supervised Learning III - Part 3
Lecture 34 - Linear Models For Classification - Part 1
Lecture 35 - Linear Models For Classification - Part 2
Lecture 36 - Linear Models For Classification - Part 3
Lecture 37 - Tree Based Methods - Part 1
Lecture 38 - Tree Based Methods - Part 2
Lecture 39 - SVMs - Part 1
Lecture 40 - SVMs - Part 2
Lecture 41 - SVMs - Part 3
Lecture 42 - Ensemble Methods - Part 1
Lecture 43 - Ensemble Methods - Part 2
Lecture 44 - Ensemble Methods - Part 3
Lecture 45 - Learning Theory - Part 1
Lecture 46 - Learning Theory - Part 2
Lecture 47 - Introduction to Probabilistic Modeling - Part 1
Lecture 48 - Introduction to Probabilistic Modeling - Part 2
Lecture 49 - Introduction to Probabilistic Modeling - Part 3
Lecture 50 - Probabilistic/Bayesian Models for Regression - Part 1
Lecture 51 - Probabilistic/Bayesian Models for Regression - Part 2
Lecture 52 - Probabilistic/Bayesian Models for Regression - Part 3
Lecture 53 - Probabilistic Classification, Latent Variable Models - Part 1
Lecture 54 - Probabilistic Classification, Latent Variable Models - Part 2
Lecture 55 - Probabilistic Classification, Latent Variable Models - Part 3
Lecture 56 - Deep Learning I - Part 1
Lecture 57 - Deep Learning I - Part 2
Lecture 58 - Deep Learning I - Part 3
Lecture 59 - Deep Learning II - Part 1
Lecture 60 - Deep Learning II - Part 2
Lecture 61 - Deep Learning II - Part 3
Lecture 62 - Deep Learning III - Part 1
Lecture 63 - Deep Learning III - Part 2
Lecture 64 - Deep Learning III - Part 3
Lecture 65 - Reinforcement learning I - Part 1
Lecture 66 - Reinforcement learning I - Part 2
Lecture 67 - Reinforcement learning II - Part 1
Lecture 68 - Reinforcement learning II - Part 2
Lecture 69 - Map-Reduce and Spark - Part 1
Lecture 70 - Map-Reduce and Spark - Part 2
Lecture 71 - Map-Reduce and Spark - Part 3
Lecture 72 - Scalable Machine Learning - Part 1
Lecture 73 - Scalable Machine Learning - Part 2

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