Machine Learning 101 : Introduction to Machine Learning

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Machine Learning 101 : Introduction to Machine Learning

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

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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;

Lecture 1: The Learning Problem

Lecture 2: Is Learning Feasible?

Lecture 3: The Linear Model I

Lecture 4: Error and Noise

Lecture 5: Training versus Testing

Lecture 6: Theory of Generalization

Lecture 7: The VC Dimension

Lecture 8: Bias-Variance Tradeoff

Lecture 9: The Linear Model II

Lecture 10: Neural Networks

Lecture 11: Overfitting

Lecture 12: Regularization

Lecture 13: Validation

Lecture 14: Support Vector Machines

Lecture 15: Kernel Methods

Lecture 16: Radial Basis Functions

Lecture 17: Three Learning Principles

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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