Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.

## About this Course

### Learner Career Outcomes

## 10%

## 14%

## Skills you will gain

### Learner Career Outcomes

## 10%

## 14%

## Offered by

### Stanford University

The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.

## Syllabus - What you will learn from this course

**2 hours to complete**

## Introduction

Welcome to Machine Learning! In this module, we introduce the core idea of teaching a computer to learn concepts using data—without being explicitly programmed. The Course Wiki is under construction. Please visit the resources tab for the most complete and up-to-date information.

**2 hours to complete**

**2 hours to complete**

## Linear Regression with One Variable

Linear regression predicts a real-valued output based on an input value. We discuss the application of linear regression to housing price prediction, present the notion of a cost function, and introduce the gradient descent method for learning.

**2 hours to complete**

**2 hours to complete**

## Linear Algebra Review

This optional module provides a refresher on linear algebra concepts. Basic understanding of linear algebra is necessary for the rest of the course, especially as we begin to cover models with multiple variables.

**2 hours to complete**

**3 hours to complete**

## Linear Regression with Multiple Variables

**3 hours to complete**

**5 hours to complete**

## Octave/Matlab Tutorial

This course includes programming assignments designed to help you understand how to implement the learning algorithms in practice. To complete the programming assignments, you will need to use Octave or MATLAB. This module introduces Octave/Matlab and shows you how to submit an assignment.

**5 hours to complete**

**2 hours to complete**

## Logistic Regression

Logistic regression is a method for classifying data into discrete outcomes. For example, we might use logistic regression to classify an email as spam or not spam. In this module, we introduce the notion of classification, the cost function for logistic regression, and the application of logistic regression to multi-class classification.

**2 hours to complete**

**5 hours to complete**

## Regularization

Machine learning models need to generalize well to new examples that the model has not seen in practice. In this module, we introduce regularization, which helps prevent models from overfitting the training data.

**5 hours to complete**

**5 hours to complete**

## Neural Networks: Representation

Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets and understand your voice commands, it is likely that a neural network is helping to understand your speech; when you cash a check, the machines that automatically read the digits also use neural networks.

**5 hours to complete**

## Reviews

### TOP REVIEWS FROM MACHINE LEARNING

The course is well organised, with cutting edge knowledge ready to use in our information era. And Andrew was really decent with clear illustration and explanations. I really enjoy taking this course!

I really enjoyed this course. I learned new exciting techniques. I think the major positive point of this course was its simple and understandable teaching method. Thanks a lot to professor Andrew Ng.

Great course. A progressive discovery of the maths inner to the learning algorithms. This course gives that insight many ML practitioners don't have and is so important for making real use cases work.

A brilliant sequence of topics and fundamentals to get a stronghold on ML . The learnings I obtained from this course will always be my guiding factor in working through the projects in my life ahead.

## Frequently Asked Questions

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