[Free Course-Udemy] – Machine Learning 101 : Introduction to machine Learning


Introductory Machine Learning course covering theory. algorithms and applications.

Introduction to machine learning

Machine learning 101: Introduction to machine learning .

Introductory Machine learning course covering theory, algorithms and applications.

This is an introductory course in machine learning(ML) that covers the basic theory, algorithms and applications. ML is a key technology in Big Data and in many financial,medical, commercial and scientific applications. It enables computational systems to adaptively improve their performance with experience accumulated from the observed data. ML has become one of the hottest fields of study today, taken up by undergraduate and graduate students from 15 different majors. This course balances theory and practice and cover the mathematical as well as the heuristic aspects. The lectures below follow each other in a story -like fashion:

  • What is leaning
  • can a machine learn?
  • How to do it?
  • How to do it well?
  • Take-home lessons

Outline of this course

  1.  Lecture 1: The learning problem
  2. Lectures 2: Is learning Feasible ?
  3. Lectures 3: The Linear Model I
  4. Lectures 4: Error and Noise
  5. Lectures 5: Training versus Testing
  6. Lectures 6 Theory of Generalization
  7. Lectures 7:The VC Dimension
  8. Lectures 8: Bias- Variance Tradeoff
  9. Lectures 9: The Linear model II
  10.  Lectures 10: Neural Networks
  11. Lectures 11: Overfitting
  12. Lectures 12 : Regularization
  13. Lectures 13: Validation
  14. Lectures 14: Support Vector Machines
  15. Lectures 15: Kernel methods
  16. lectures 16: Radial Basics functions
  17. Lectures 17: three learning principle
  18. Lectures 18: Epilogue

This course has some videos on youtube that has creative commen licence(cc)

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