Machine Learning.png

Machine Learning

by Andrew Ng

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Course Description

In this course, you'll learn about some of the most widely used and successful machine learning techniques. You'll have the opportunity to implement these algorithms yourself, and gain practice with them. You will also learn some of practical hands-on tricks and techniques (rarely discussed in textbooks) that help get learning algorithms to work well. This is an "applied" machine learning class, and we emphasize the intuitions and know-how needed to get learning algorithms to work in practice, rather than the mathematical derivations.
Familiarity with programming, basic linear algebra (matrices, vectors, matrix-vector multiplication), and basic probability (random variables, basic properties of probability) is assumed. Basic calculus (derivatives and partial derivatives) would be helpful and would give you additional intuitions about the algorithms, but isn't required to fully complete this course.

I. INTRODUCTION

Welcome

What is Machine Learning?

Supervised Learning Introduction

Unsupervised Learning Introduction

Installing Octave

II. LINEAR REGRESSION I

Supervised Learning Introduction(1.2x)(1.5x)

Model Representation(1.2x)(1.5x)

Cost Function(1.2x)(1.5x)

Gradient Descent(1.2x)(1.5x)

Gradient Descent for Linear Regression(1.2x)(1.5x)

Vectorized Implementation(1.2x)(1.5x)

Exercise 2

III. LINEAR REGRESSION II

Feature Scaling(1.2x)(1.5x)

Learning Rate(1.2x)(1.5x)

Features and Polynomial Regression(1.2x)(1.5x)

Normal Equations(1.2x)(1.5x)

Exercise 3

IV. LOGISTIC REGRESSION

Classification(1.2x)(1.5x)

Model(1.2x)(1.5x)

Optimization Objective I(1.2x)(1.5x)

Optimization Objective II(1.2x)(1.5x)

Gradient Descent(1.2x)(1.5x)

Newton's Method I(1.2x)(1.5x)

Newton's Method II(1.2x)(1.5x)

Gradient Descent vs Newton's Method(1.2x)(1.5x)

Exercise 4

V. REGULARIZATION

The Problem Of Overfitting(1.2x)(1.5x)

Optimization Objective(1.2x)(1.5x)

Common Variations(1.2x)(1.5x)

Regularized Linear Regression(1.2x)(1.5x)

Regularized Logistic Regression(1.2x)(1.5x)

Exercise 5

VI. NAIVE BAYES

Generative Learning Algorithms(1.2x)(1.5x)

Text Classification(1.2x)(1.5x)

Exercise 6

VII.

Exercise 7

VIII.

Exercise 8

IX.

Exercise 9
from stanford Machine Learning OpenClassroom