Machine Learning by Stanford University | Coursera Explore For EnterpriseFor Students Browse Top Courses Log In Join for Free Browse Data Science Machine Learning Machine Learning 4.9stars 152,260 ratings | 97% Andrew Ng    Top Instructor Offered By About Instructors Syllabus Reviews Enrollment Options FAQ Machine Learning Stanford University About Instructors Syllabus Reviews Enrollment Options FAQ About this Course 7,310,661 recent views 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. Learner Career Outcomes 36% started a new career after completing these courses 34% got a tangible career benefit from this course Shareable Certificate Earn a Certificate upon completion 100% online Start instantly and learn at your own schedule. Flexible deadlines Reset deadlines in accordance to your schedule. Approx. 60 hours to complete English Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, Turkish, English, Hebrew, Spanish, Hindi, Japanese Skills you will gain Logistic RegressionArtificial Neural NetworkMachine Learning (ML) AlgorithmsMachine Learning Learner Career Outcomes 36% started a new career after completing these courses 34% got a tangible career benefit from this course Shareable Certificate Earn a Certificate upon completion 100% online Start instantly and learn at your own schedule. Flexible deadlines Reset deadlines in accordance to your schedule. Approx. 60 hours to complete English Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, Turkish, English, Hebrew, Spanish, Hindi, Japanese Instructor Instructor rating4.93/5 (20,548 Ratings) Andrew Ng Top Instructor InstructorFounder, DeepLearning.AI & Co-founder, Coursera 4,620,667 Learners 11 Courses 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 Content Rating 97%(1,303,446 ratings)Week 1 Week 1 2 hours to completeIntroduction 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 5 videos (Total 42 min), 9 readings, 1 quiz See All 5 videosWelcome to Machine Learning!1m Welcome6m What is Machine Learning?7m Supervised Learning12m Unsupervised Learning14m 9 readingsMachine 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 1 practice exerciseIntroduction30m 2 hours to completeLinear 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 7 videos (Total 70 min), 8 readings, 1 quiz See All 7 videosModel Representation8m Cost Function8m Cost Function - Intuition I11m Cost Function - Intuition II8m Gradient Descent11m Gradient Descent Intuition11m Gradient Descent For Linear Regression10m 8 readingsModel Representation3m Cost Function3m Cost Function - Intuition I4m Cost Function - Intuition II3m Gradient Descent3m Gradient Descent Intuition3m Gradient Descent For Linear Regression6m Lecture Slides20m 1 practice exerciseLinear Regression with One Variable30m 2 hours to completeLinear 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 6 videos (Total 61 min), 7 readings, 1 quiz See All 6 videosMatrices and Vectors8m Addition and Scalar Multiplication6m Matrix Vector Multiplication13m Matrix Matrix Multiplication11m Matrix Multiplication Properties9m Inverse and Transpose11m 7 readingsMatrices and Vectors2m Addition and Scalar Multiplication3m Matrix Vector Multiplication2m Matrix Matrix Multiplication2m Matrix Multiplication Properties2m Inverse and Transpose3m Lecture Slides10m 1 practice exerciseLinear Algebra30m Week 2 Week 2 3 hours to completeLinear 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. 3 hours to complete 8 videos (Total 65 min), 16 readings, 1 quiz See All 8 videosMultiple Features8m 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 16 readingsSetting Up Your Programming Assignment Environment8m Access to MATLAB Online and the Exercise Files for MATLAB Users3m 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 1 practice exerciseLinear Regression with Multiple Variables30m 5 hours to completeOctave/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 6 videos (Total 80 min), 1 reading, 2 quizzes See All 6 videosBasic Operations13m Moving Data Around16m Computing on Data13m Plotting Data9m Control Statements: for, while, if statement12m Vectorization13m 1 readingLecture Slides10m 1 practice exerciseOctave/Matlab Tutorial30m Week 3 Week 3 2 hours to completeLogistic 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 7 videos (Total 71 min), 8 readings, 1 quiz See All 7 videosClassification8m Hypothesis Representation7m Decision Boundary14m Cost Function10m Simplified Cost Function and Gradient Descent10m Advanced Optimization14m Multiclass Classification: One-vs-all6m 8 readingsClassification2m Hypothesis Representation3m Decision Boundary3m Cost Function3m Simplified Cost Function and Gradient Descent3m Advanced Optimization3m Multiclass Classification: One-vs-all3m Lecture Slides10m 1 practice exerciseLogistic Regression30m 5 hours to completeRegularization 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 4 videos (Total 39 min), 5 readings, 2 quizzes See All 4 videosThe Problem of Overfitting9m Cost Function10m Regularized Linear Regression10m Regularized Logistic Regression8m 5 readingsThe Problem of Overfitting3m Cost Function3m Regularized Linear Regression3m Regularized Logistic Regression3m Lecture Slides10m 1 practice exerciseRegularization30m Week 4 Week 4 5 hours to completeNeural 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 7 videos (Total 63 min), 6 readings, 2 quizzes See All 7 videosNon-linear Hypotheses9m Neurons and the Brain7m Model Representation I12m Model Representation II11m Examples and Intuitions I7m Examples and Intuitions II10m Multiclass Classification3m 6 readingsModel Representation I6m Model Representation II6m Examples and Intuitions I2m Examples and Intuitions II3m Multiclass Classification3m Lecture Slides10m 1 practice exerciseNeural Networks: Representation30m Show More Week 5 Week 5 5 hours to completeNeural 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. 5 hours to complete 8 videos (Total 78 min), 8 readings, 2 quizzes See All 8 videosCost Function6m Backpropagation Algorithm11m Backpropagation Intuition12m Implementation Note: Unrolling Parameters7m Gradient Checking11m Random Initialization6m Putting It Together13m Autonomous Driving6m 8 readingsCost Function4m Backpropagation Algorithm10m Backpropagation Intuition4m Implementation Note: Unrolling Parameters3m Gradient Checking3m Random Initialization3m Putting It Together4m Lecture Slides10m 1 practice exerciseNeural Networks: Learning30m Week 6 Week 6 5 hours to completeAdvice 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. 5 hours to complete 7 videos (Total 63 min), 7 readings, 2 quizzes See All 7 videosDeciding What to Try Next5m Evaluating a Hypothesis7m Model Selection and Train/Validation/Test Sets12m Diagnosing Bias vs. Variance7m Regularization and Bias/Variance11m Learning Curves11m Deciding What to Do Next Revisited6m 7 readingsEvaluating a Hypothesis4m Model Selection and Train/Validation/Test Sets3m Diagnosing Bias vs. Variance3m Regularization and Bias/Variance3m Learning Curves3m Deciding What to do Next Revisited3m Lecture Slides10m 1 practice exerciseAdvice for Applying Machine Learning30m 2 hours to completeMachine 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. 2 hours to complete 5 videos (Total 60 min), 3 readings, 1 quiz See All 5 videosPrioritizing What to Work On9m Error Analysis13m Error Metrics for Skewed Classes11m Trading Off Precision and Recall14m Data For Machine Learning11m 3 readingsPrioritizing What to Work On3m Error Analysis3m Lecture Slides10m 1 practice exerciseMachine Learning System Design30m Week 7 Week 7 5 hours to completeSupport 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. 5 hours to complete 6 videos (Total 98 min), 1 reading, 2 quizzes See All 6 videosOptimization Objective14m Large Margin Intuition10m Mathematics Behind Large Margin Classification19m Kernels I15m Kernels II15m Using An SVM21m 1 readingLecture Slides10m 1 practice exerciseSupport Vector Machines30m Week 8 Week 8 1 hour to completeUnsupervised 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. 1 hour to complete 5 videos (Total 39 min), 1 reading, 1 quiz See All 5 videosUnsupervised Learning: Introduction3m K-Means Algorithm12m Optimization Objective7m Random Initialization7m Choosing the Number of Clusters8m 1 readingLecture Slides10m 1 practice exerciseUnsupervised Learning30m 5 hours to completeDimensionality 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. 5 hours to complete 7 videos (Total 67 min), 1 reading, 2 quizzes See All 7 videosMotivation I: Data Compression10m Motivation II: Visualization5m Principal Component Analysis Problem Formulation9m Principal Component Analysis Algorithm15m Reconstruction from Compressed Representation3m Choosing the Number of Principal Components10m Advice for Applying PCA12m 1 readingLecture Slides10m 1 practice exercisePrincipal Component Analysis30m Week 9 Week 9 2 hours to completeAnomaly 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. 2 hours to complete 8 videos (Total 91 min), 1 reading, 1 quiz See All 8 videosProblem Motivation7m Gaussian Distribution10m Algorithm12m Developing and Evaluating an Anomaly Detection System13m Anomaly Detection vs. Supervised Learning7m Choosing What Features to Use12m Multivariate Gaussian Distribution13m Anomaly Detection using the Multivariate Gaussian Distribution14m 1 readingLecture Slides10m 1 practice exerciseAnomaly Detection30m 5 hours to completeRecommender 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. 5 hours to complete 6 videos (Total 58 min), 1 reading, 2 quizzes See All 6 videosProblem Formulation7m Content Based Recommendations14m Collaborative Filtering10m Collaborative Filtering Algorithm8m Vectorization: Low Rank Matrix Factorization8m Implementational Detail: Mean Normalization8m 1 readingLecture Slides10m 1 practice exerciseRecommender Systems30m Week 10 Week 10 2 hours to completeLarge 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. 2 hours to complete 6 videos (Total 64 min), 1 reading, 1 quiz See All 6 videosLearning With Large Datasets5m Stochastic Gradient Descent13m Mini-Batch Gradient Descent6m Stochastic Gradient Descent Convergence11m Online Learning12m Map Reduce and Data Parallelism14m 1 readingLecture Slides10m 1 practice exerciseLarge Scale Machine Learning30m Week 11 Week 11 2 hours to completeApplication 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. 2 hours to complete 5 videos (Total 57 min), 1 reading, 1 quiz See All 5 videosProblem Description and Pipeline7m Sliding Windows14m Getting Lots of Data and Artificial Data16m Ceiling Analysis: What Part of the Pipeline to Work on Next13m Summary and Thank You4m 1 readingLecture Slides10m 1 practice exerciseApplication: Photo OCR30m Reviews 4.9 38859 reviews 5 stars 92.47% 4 stars 6.88% 3 stars 0.45% 2 stars 0.08% 1 star 0.10% TOP REVIEWS FROM MACHINE LEARNING by MNOct 30, 2017 Great overview, enough details to have a good understanding of why the techniques work well. Especially appreciated the practical advice regarding debugging, algorithm evaluation and ceiling analysis. by PZJun 29, 2020 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. by RKAug 19, 2019 It is the best online course for any person wanna learn machine learning. Andrew sir teaches very well. His pace is very good. The insights which you will get in this course turns out to be wonderful. by TPJun 25, 2020 This course is a very applicable. Professor Ng explains precisely each algorithm and even tries to give an intuition for mathematical and statistic concepts behind each algorithm. Thank you very much. View all reviews Frequently Asked Questions When will I have access to the lectures and assignments? Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option: The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience. 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. Is financial aid available? Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. 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