Machine Learning

Location type
Logo Stanford University Open Classroom

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Description

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 variab…

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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

Teacher: Andrew Ng

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There are no frequently asked questions yet. Send an Email to info@springest.com