NOC:Introduction to Data Analytics Video Lectures

NOC:Introduction to Data Analytics
'NOC:Introduction to Data Analytics' Video Lectures by Dr. Nandan Sudarsanam, Dr. Balaraman Ravindran from IIT Madras
"NOC:Introduction to Data Analytics" - Video Lectures
1. Course Overview
2. Course Overview (cont'd)
3. Descriptive Statistics - Graphical Approaches
4. Descriptive Statistics - Measures of Central Tendency
5. Descriptive Statistics - Measures of Dispersion
6. Random Variables and Probability Distributions
7. Probability Distributions(contd)
8. Probability Distributions(contd)
9. Inferential Statistics - Motivation
10. Inferential Statistics - Single sample tests
11. Two Sample tests
12. Type 1 and Type 2 Errors
13. Confidence Intervals
14. ANOVA and Test of Independence
15. Short Introduction to Regression
16. Introduction to Machine Learning
17. Supervised Learning
18. Unsupervised Learning
19. Ordinary Least Squares Regression
20. Simple and Multiple Regression in Excel and Matlab
21. Regularization/ Coefficients Shrinkage
22. Data Modelling and Algorithmic Modelling Approaches
23. Logistic Regression
24. Training a Logistic Regression Classifier
25. Classification and Regression Trees
26. Classification and Regression Trees(contd)
27. Bias Variance Dichotomy
28. Model Assessment and Selection
29. Support Vector Machines
30. Support Vector Machines(contd)
31. Support Vector Machines for Non Linearly Separable Data
32. Support Vector Machines and Kernel Transformations
33. Ensemble Methods and Random Forests
34. Artificial Neural Networks
35. Artificial Neural Networks(cont\'d)
36. Deep Learning
37. Associative Rule Mining
38. Association Rule Mining (contd)
39. Big Data, A small introduction
40. Big Data - A small introduction (contd)
41. Clustering Analysis
42. Clustering Analysis (contd)
43. Introduction to Experimentation and Active Learning
44. Introduction to Experimentation and Active Learning(contd)
45. An Introduction to Online Learning - Reinforcement Learning
46. An Introduction to Online Learning - Reinforcement Learning (contd)
47. Summary+ Insights into the Final Exam
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