Offered By

Stanford University

About this Course

4.9

85,772 ratings

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22,039 reviews

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.
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

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Logistic RegressionArtificial Neural NetworkMachine Learning (ML) AlgorithmsMachine Learning

Start instantly and learn at your own schedule.

Reset deadlines in accordance to your schedule.

Suggested: 7 hours/week...

Subtitles: English, Chinese (Simplified), Hebrew, Spanish, Hindi, Japanese...

Week

1Welcome 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....

5 videos (Total 42 min), 9 readings, 1 quiz

Welcome6m

What is Machine Learning?7m

Supervised Learning12m

Unsupervised Learning14m

Machine Learning Honor Code8m

What is Machine Learning?5m

How to Use Discussion Forums4m

Supervised Learning4m

Unsupervised Learning3m

Who are Mentors?3m

Get to Know Your Classmates8m

Frequently Asked Questions11m

Lecture Slides20m

Introduction10m

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....

7 videos (Total 70 min), 8 readings, 1 quiz

Cost Function8m

Cost Function - Intuition I11m

Cost Function - Intuition II8m

Gradient Descent11m

Gradient Descent Intuition11m

Gradient Descent For Linear Regression10m

Model Representation3m

Cost Function3m

Cost Function - Intuition I4m

Cost Function - Intuition II3m

Gradient Descent3m

Gradient Descent Intuition3m

Gradient Descent For Linear Regression6m

Lecture Slides20m

Linear Regression with One Variable10m

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....

6 videos (Total 61 min), 7 readings, 1 quiz

Addition and Scalar Multiplication6m

Matrix Vector Multiplication13m

Matrix Matrix Multiplication11m

Matrix Multiplication Properties9m

Inverse and Transpose11m

Matrices and Vectors2m

Addition and Scalar Multiplication3m

Matrix Vector Multiplication2m

Matrix Matrix Multiplication2m

Matrix Multiplication Properties2m

Inverse and Transpose3m

Lecture Slides10m

Linear Algebra10m

Week

2
What if your input has more than one value? In this module, we show how linear regression can be extended to accommodate multiple input features. We also discuss best practices for implementing linear regression....

8 videos (Total 65 min), 16 readings, 1 quiz

Gradient Descent for Multiple Variables5m

Gradient Descent in Practice I - Feature Scaling8m

Gradient Descent in Practice II - Learning Rate8m

Features and Polynomial Regression7m

Normal Equation16m

Normal Equation Noninvertibility5m

Working on and Submitting Programming Assignments3m

Setting Up Your Programming Assignment Environment8m

Accessing MATLAB Online and Uploading the Exercise Files3m

Installing Octave on Windows3m

Installing Octave on Mac OS X (10.10 Yosemite and 10.9 Mavericks and Later)10m

Installing Octave on Mac OS X (10.8 Mountain Lion and Earlier)3m

Installing Octave on GNU/Linux7m

More Octave/MATLAB resources10m

Multiple Features3m

Gradient Descent For Multiple Variables2m

Gradient Descent in Practice I - Feature Scaling3m

Gradient Descent in Practice II - Learning Rate4m

Features and Polynomial Regression3m

Normal Equation3m

Normal Equation Noninvertibility2m

Programming tips from Mentors10m

Lecture Slides20m

Linear Regression with Multiple Variables10m

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....

6 videos (Total 80 min), 1 reading, 2 quizzes

Moving Data Around16m

Computing on Data13m

Plotting Data9m

Control Statements: for, while, if statement12m

Vectorization13m

Lecture Slides10m

Octave/Matlab Tutorial10m

Week

3Logistic 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.
...

7 videos (Total 71 min), 8 readings, 1 quiz

Hypothesis Representation7m

Decision Boundary14m

Cost Function10m

Simplified Cost Function and Gradient Descent10m

Advanced Optimization14m

Multiclass Classification: One-vs-all6m

Classification2m

Hypothesis Representation3m

Decision Boundary3m

Cost Function3m

Simplified Cost Function and Gradient Descent3m

Advanced Optimization3m

Multiclass Classification: One-vs-all3m

Lecture Slides10m

Logistic Regression10m

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. ...

4 videos (Total 39 min), 5 readings, 2 quizzes

Cost Function10m

Regularized Linear Regression10m

Regularized Logistic Regression8m

The Problem of Overfitting3m

Cost Function3m

Regularized Linear Regression3m

Regularized Logistic Regression3m

Lecture Slides10m

Regularization10m

Week

4Neural 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. ...

7 videos (Total 63 min), 6 readings, 2 quizzes

Neurons and the Brain7m

Model Representation I12m

Model Representation II11m

Examples and Intuitions I7m

Examples and Intuitions II10m

Multiclass Classification3m

Model Representation I6m

Model Representation II6m

Examples and Intuitions I2m

Examples and Intuitions II3m

Multiclass Classification3m

Lecture Slides10m

Neural Networks: Representation10m

4.9

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got a tangible career benefit from this course

By DW•Feb 20th 2016

Fantastic intro to the fundamentals of machine learning. If you want to take your understanding of machine learning concepts beyond "model.fit(X, Y), model.predict(X)" then this is the course for you.

By JP•Oct 25th 2016

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.

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....

When will I have access to the lectures and assignments?

Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

What will I get if I purchase the Certificate?

When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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