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Machine Learning 101 : Introduction to Machine Learning

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Introductory Machine Learning course covering theory, algorithms and applications.

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Description

What you’ll learn
  • The Learning Problem
  • Learning from Data
  • Is Learning Feasible?
  • The Linear Model
  • Error and Noise
  • Training versus Testing
  • Theory of Generalization
  • The VC Dimension
  • Bias-Variance Tradeoff
  • Neural Networks
  • Overfitting
  • Regularization
  • Validation
  • Support Vector Machines
  • Kernel Methods
  • Radial Basis Functions
  • Three Learning Principles
  • Epilogue
  • What is learning?
  • Can a machine learn?
  • Identify basic theoretical principles, algorithms, and applications of Machine Learning
  • Elaborate on the connections between theory and practice in Machine Learning
  • Master the mathematical and heuristic aspects of Machine Learning and their applications to real world situations
Requirements
  • Anyone who interest Machine Learning can take this course
Description

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 covers the mathematical as well as the heuristic aspects. The lectures below follow each other in a story-like fashion:

  • What is learning?
  • 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. Lecture 2: Is Learning Feasible?
  3. Lecture 3: The Linear Model I
  4. Lecture 4: Error and Noise
  5. Lecture 5: Training versus Testing
  6. Lecture 6: Theory of Generalization
  7. Lecture 7: The VC Dimension
  8. Lecture 8: Bias-Variance Tradeoff
  9. Lecture 9: The Linear Model II
  10. Lecture 10: Neural Networks
  11. Lecture 11: Overfitting
  12. Lecture 12: Regularization
  13. Lecture 13: Validation
  14. Lecture 14: Support Vector Machines
  15. Lecture 15: Kernel Methods
  16. Lecture 16: Radial Basis Functions
  17. Lecture 17: Three Learning Principles
  18. Lecture 18: Epilogue

This course has some videos on youtube that has Creative Commen Licence (CC).

Who this course is for:
  • If you have no prior coding or scripting experience, you can also attend this lesson.
  • Anyone who interest Data Science
  • Anyone who interest Learning From Data
  • Anyone who interest how deep learning really works
  • Software developers or programmers who want to transition into the lucrative data science and machine learning career path will learn a lot from this course.

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