Machine Learning

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About this course: 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 th…

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Didn't find what you were looking for? See also: Speech, Machine Learning, Innovation, Hour of Code, and Python.

When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: 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.

Created by:  Stanford University
  • Taught by:  Andrew Ng, Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain

Language English, Subtitles: Spanish, Hindi, Japanese, Chinese (Simplified) How To Pass Pass all graded assignments to complete the course. User Ratings 4.9 stars Average User Rating 4.9See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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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


WEEK 1


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.


5 videos, 9 readings expand


  1. Video: Welcome to Machine Learning!
  2. Reading: Machine Learning Honor Code
  3. Video: Welcome
  4. Video: What is Machine Learning?
  5. Reading: What is Machine Learning?
  6. Reading: How to Use Discussion Forums
  7. Video: Supervised Learning
  8. Reading: Supervised Learning
  9. Video: Unsupervised Learning
  10. Reading: Unsupervised Learning
  11. Reading: Who are Mentors?
  12. Reading: Get to Know Your Classmates
  13. Reading: Frequently Asked Questions
  14. Reading: Lecture Slides

Graded: Introduction

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.


7 videos, 8 readings expand


  1. Video: Model Representation
  2. Reading: Model Representation
  3. Video: Cost Function
  4. Reading: Cost Function
  5. Video: Cost Function - Intuition I
  6. Reading: Cost Function - Intuition I
  7. Video: Cost Function - Intuition II
  8. Reading: Cost Function - Intuition II
  9. Video: Gradient Descent
  10. Reading: Gradient Descent
  11. Video: Gradient Descent Intuition
  12. Reading: Gradient Descent Intuition
  13. Video: Gradient Descent For Linear Regression
  14. Reading: Gradient Descent For Linear Regression
  15. Reading: Lecture Slides

Graded: Linear Regression with One Variable

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.


6 videos, 7 readings, 1 practice quiz expand


  1. Video: Matrices and Vectors
  2. Reading: Matrices and Vectors
  3. Video: Addition and Scalar Multiplication
  4. Reading: Addition and Scalar Multiplication
  5. Video: Matrix Vector Multiplication
  6. Reading: Matrix Vector Multiplication
  7. Video: Matrix Matrix Multiplication
  8. Reading: Matrix Matrix Multiplication
  9. Video: Matrix Multiplication Properties
  10. Reading: Matrix Multiplication Properties
  11. Video: Inverse and Transpose
  12. Reading: Inverse and Transpose
  13. Reading: Lecture Slides
  14. Practice Quiz: Linear Algebra


WEEK 2


Linear Regression with Multiple Variables
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, 16 readings expand


  1. Reading: Setting Up Your Programming Assignment Environment
  2. Reading: Installing MATLAB
  3. Reading: Installing Octave on Windows
  4. Reading: Installing Octave on Mac OS X (10.10 Yosemite and 10.9 Mavericks and Later)
  5. Reading: Installing Octave on Mac OS X (10.8 Mountain Lion and Earlier)
  6. Reading: Installing Octave on GNU/Linux
  7. Reading: Octave/MATLAB resources
  8. Video: Multiple Features
  9. Reading: Multiple Features
  10. Video: Gradient Descent for Multiple Variables
  11. Reading: Gradient Descent For Multiple Variables
  12. Video: Gradient Descent in Practice I - Feature Scaling
  13. Reading: Gradient Descent in Practice I - Feature Scaling
  14. Video: Gradient Descent in Practice II - Learning Rate
  15. Reading: Gradient Descent in Practice II - Learning Rate
  16. Video: Features and Polynomial Regression
  17. Reading: Features and Polynomial Regression
  18. Video: Normal Equation
  19. Reading: Normal Equation
  20. Video: Normal Equation Noninvertibility
  21. Reading: Normal Equation Noninvertibility
  22. Video: Working on and Submitting Programming Assignments
  23. Reading: Programming tips from Mentors
  24. Reading: Lecture Slides

Graded: Linear Regression with Multiple Variables

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.


6 videos, 1 reading expand


  1. Video: Basic Operations
  2. Video: Moving Data Around
  3. Video: Computing on Data
  4. Video: Plotting Data
  5. Video: Control Statements: for, while, if statement
  6. Video: Vectorization
  7. Reading: Lecture Slides
  8. Programming: Linear Regression

Graded: Octave/Matlab Tutorial

WEEK 3


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.


7 videos, 8 readings expand


  1. Video: Classification
  2. Reading: Classification
  3. Video: Hypothesis Representation
  4. Reading: Hypothesis Representation
  5. Video: Decision Boundary
  6. Reading: Decision Boundary
  7. Video: Cost Function
  8. Reading: Cost Function
  9. Video: Simplified Cost Function and Gradient Descent
  10. Reading: Simplified Cost Function and Gradient Descent
  11. Video: Advanced Optimization
  12. Reading: Advanced Optimization
  13. Video: Multiclass Classification: One-vs-all
  14. Reading: Multiclass Classification: One-vs-all
  15. Reading: Lecture Slides

Graded: Logistic Regression

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.


4 videos, 5 readings expand


  1. Video: The Problem of Overfitting
  2. Reading: The Problem of Overfitting
  3. Video: Cost Function
  4. Reading: Cost Function
  5. Video: Regularized Linear Regression
  6. Reading: Regularized Linear Regression
  7. Video: Regularized Logistic Regression
  8. Reading: Regularized Logistic Regression
  9. Reading: Lecture Slides
  10. Programming: Logistic Regression

Graded: Regularization

WEEK 4


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.


7 videos, 6 readings expand


  1. Video: Non-linear Hypotheses
  2. Video: Neurons and the Brain
  3. Video: Model Representation I
  4. Reading: Model Representation I
  5. Video: Model Representation II
  6. Reading: Model Representation II
  7. Video: Examples and Intuitions I
  8. Reading: Examples and Intuitions I
  9. Video: Examples and Intuitions II
  10. Reading: Examples and Intuitions II
  11. Video: Multiclass Classification
  12. Reading: Multiclass Classification
  13. Reading: Lecture Slides
  14. Programming: Multi-class Classification and Neural Networks

Graded: Neural Networks: Representation

WEEK 5


Neural Networks: Learning
In this module, we introduce the backpropagation algorithm that is used to help learn parameters for a neural network. At the end of this module, you will be implementing your own neural network for digit recognition.


8 videos, 8 readings expand


  1. Video: Cost Function
  2. Reading: Cost Function
  3. Video: Backpropagation Algorithm
  4. Reading: Backpropagation Algorithm
  5. Video: Backpropagation Intuition
  6. Reading: Backpropagation Intuition
  7. Video: Implementation Note: Unrolling Parameters
  8. Reading: Implementation Note: Unrolling Parameters
  9. Video: Gradient Checking
  10. Reading: Gradient Checking
  11. Video: Random Initialization
  12. Reading: Random Initialization
  13. Video: Putting It Together
  14. Reading: Putting It Together
  15. Video: Autonomous Driving
  16. Reading: Lecture Slides
  17. Programming: Neural Network Learning

Graded: Neural Networks: Learning

WEEK 6


Advice for Applying Machine Learning
Applying machine learning in practice is not always straightforward. In this module, we share best practices for applying machine learning in practice, and discuss the best ways to evaluate performance of the learned models.


7 videos, 7 readings expand


  1. Video: Deciding What to Try Next
  2. Video: Evaluating a Hypothesis
  3. Reading: Evaluating a Hypothesis
  4. Video: Model Selection and Train/Validation/Test Sets
  5. Reading: Model Selection and Train/Validation/Test Sets
  6. Video: Diagnosing Bias vs. Variance
  7. Reading: Diagnosing Bias vs. Variance
  8. Video: Regularization and Bias/Variance
  9. Reading: Regularization and Bias/Variance
  10. Video: Learning Curves
  11. Reading: Learning Curves
  12. Video: Deciding What to Do Next Revisited
  13. Reading: Deciding What to do Next Revisited
  14. Reading: Lecture Slides
  15. Programming: Regularized Linear Regression and Bias/Variance

Graded: Advice for Applying Machine Learning

Machine Learning System Design



To optimize a machine learning algorithm, you’ll need to first understand where the biggest improvements can be made. In this module, we discuss how to understand the performance of a machine learning system with multiple parts, and also how to deal with skewed data.


5 videos, 3 readings expand


  1. Video: Prioritizing What to Work On
  2. Reading: Prioritizing What to Work On
  3. Video: Error Analysis
  4. Reading: Error Analysis
  5. Video: Error Metrics for Skewed Classes
  6. Video: Trading Off Precision and Recall
  7. Video: Data For Machine Learning
  8. Reading: Lecture Slides

Graded: Machine Learning System Design

WEEK 7


Support Vector Machines
Support vector machines, or SVMs, is a machine learning algorithm for classification. We introduce the idea and intuitions behind SVMs and discuss how to use it in practice.


6 videos, 1 reading expand


  1. Video: Optimization Objective
  2. Video: Large Margin Intuition
  3. Video: Mathematics Behind Large Margin Classification
  4. Video: Kernels I
  5. Video: Kernels II
  6. Video: Using An SVM
  7. Reading: Lecture Slides
  8. Programming: Support Vector Machines

Graded: Support Vector Machines

WEEK 8


Unsupervised Learning
We use unsupervised learning to build models that help us understand our data better. We discuss the k-Means algorithm for clustering that enable us to learn groupings of unlabeled data points.


5 videos, 1 reading expand


  1. Video: Unsupervised Learning: Introduction
  2. Video: K-Means Algorithm
  3. Video: Optimization Objective
  4. Video: Random Initialization
  5. Video: Choosing the Number of Clusters
  6. Reading: Lecture Slides

Graded: Unsupervised Learning

Dimensionality Reduction
In this module, we introduce Principal Components Analysis, and show how it can be used for data compression to speed up learning algorithms as well as for visualizations of complex datasets.


7 videos, 1 reading expand


  1. Video: Motivation I: Data Compression
  2. Video: Motivation II: Visualization
  3. Video: Principal Component Analysis Problem Formulation
  4. Video: Principal Component Analysis Algorithm
  5. Video: Reconstruction from Compressed Representation
  6. Video: Choosing the Number of Principal Components
  7. Video: Advice for Applying PCA
  8. Reading: Lecture Slides
  9. Programming: K-Means Clustering and PCA

Graded: Principal Component Analysis

WEEK 9


Anomaly Detection



Given a large number of data points, we may sometimes want to figure out which ones vary significantly from the average. For example, in manufacturing, we may want to detect defects or anomalies. We show how a dataset can be modeled using a Gaussian distribution, and how the model can be used for anomaly detection.


8 videos, 1 reading expand


  1. Video: Problem Motivation
  2. Video: Gaussian Distribution
  3. Video: Algorithm
  4. Video: Developing and Evaluating an Anomaly Detection System
  5. Video: Anomaly Detection vs. Supervised Learning
  6. Video: Choosing What Features to Use
  7. Video: Multivariate Gaussian Distribution
  8. Video: Anomaly Detection using the Multivariate Gaussian Distribution
  9. Reading: Lecture Slides

Graded: Anomaly Detection

Recommender Systems



When you buy a product online, most websites automatically recommend other products that you may like. Recommender systems look at patterns of activities between different users and different products to produce these recommendations. In this module, we introduce recommender algorithms such as the collaborative filtering algorithm and low-rank matrix factorization.


6 videos, 1 reading expand


  1. Video: Problem Formulation
  2. Video: Content Based Recommendations
  3. Video: Collaborative Filtering
  4. Video: Collaborative Filtering Algorithm
  5. Video: Vectorization: Low Rank Matrix Factorization
  6. Video: Implementational Detail: Mean Normalization
  7. Reading: Lecture Slides
  8. Programming: Anomaly Detection and Recommender Systems

Graded: Recommender Systems

WEEK 10


Large Scale Machine Learning
Machine learning works best when there is an abundance of data to leverage for training. In this module, we discuss how to apply the machine learning algorithms with large datasets.


6 videos, 1 reading expand


  1. Video: Learning With Large Datasets
  2. Video: Stochastic Gradient Descent
  3. Video: Mini-Batch Gradient Descent
  4. Video: Stochastic Gradient Descent Convergence
  5. Video: Online Learning
  6. Video: Map Reduce and Data Parallelism
  7. Reading: Lecture Slides

Graded: Large Scale Machine Learning

WEEK 11


Application Example: Photo OCR
Identifying and recognizing objects, words, and digits in an image is a challenging task. We discuss how a pipeline can be built to tackle this problem and how to analyze and improve the performance of such a system.


5 videos, 1 reading expand


  1. Video: Problem Description and Pipeline
  2. Video: Sliding Windows
  3. Video: Getting Lots of Data and Artificial Data
  4. Video: Ceiling Analysis: What Part of the Pipeline to Work on Next
  5. Reading: Lecture Slides
  6. Video: Summary and Thank You

Graded: Application: Photo OCR
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