Back to Machine Learning

stars

155,957 ratings

•

39,873 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....

TP

Jun 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.

AD

Apr 21, 2017

Very good coverage of different supervised and unsupervised algorithms, and lots of practical insights around implementation. All the explanations provided helped to understand the concepts very well.

Filter by:

By BAPPADITYA D

•Sep 6, 2017

This is the course through which I started my journey with machine learning. I am really grateful to the Machine Learning community and specially Prof. Ng for making this course and the curricular available to so many students including me. I learned a lot from this course and beside this course helps me to think and dive into the deep of ML. Though I completed this course and got my certificate but no doubt I am surely going to miss this course. But I already started my journey with Prof. Ng once again with his deep learning course. Once again, I am thankful to the Coursera community, the Machine Learning community, the discussion forum members and last but not the least Prof. Ng for their tremendous effort, support and providing us the platform to learn and implement the crux of Machine Learning. A recommended course for researcher, self-learning student, industry personnel related with AI.

By C S

•Aug 29, 2017

This is an exceptionally good course where I learnt the fundamentals of machine learning in detail. The coding assignments "concretely" helped me commit these concepts to memory, give one a hands on feel for problem solving and it's just awesome to see your own work. The course is a little rough at the start. Once the coding assignments become more image based though it becomes easier to understand and visualise things.

The course in my opinion requires some linera algebra background. It is very time consuming, not only is 12 weeks long but coding assignments rarely take 3 hours as stipulated.

The course can be refered back to when looking for information about machine learning as it is well organised and contains high level problem solving info. I feel that I have a better understanding of what coursera was built for and where online learnings place is in the world after completing this course.

By Glen D

•Sep 17, 2018

Dr. Ng is an incredible instructor! The lectures meticulously lay out the math required and the instructor provides encouraging comments along the way. I was not looking forward to the math in this course but ultimately is was not only straightforward, but addicting! I started working a little ahead, then farther and farther ahead. I actually started wanting to spend all my free time on this, doing all the optional work, and taking the example code and expanding upon it for my own experiments. And now I have finished the course in about a month, and I am genuinely sad it is over. Why couldn't I have been this motivated about my classes when I was a university student years ago? Probably because Dr. Ng wasn't there! Great course. Excellent instructor. Lots of resource materials when you need them. Ex4 is a bit difficult and time-consuming but all the rest were very fast to complete.

By Sumit K

•Aug 18, 2019

I will never be able to thank Sir Andrew Ng and team Coursera enough for this opportunity. Sir Andrew Ng is truly a genius. I have learned a lot here not just technical skills but also way of thinking and approaching problems in general. I feel like growing up as a person here, and that's how machine learning also works. Isn't it? I'm a bioinformatics student, so I needed this skill. I've already started thinking of on how I can apply these algorithms on biological data to solve problems in genetics. Lastly, I'm convinced by others that this is the best course on machine learning. I'm not in a state or position to review all the efforts you guys have put to make such complicated looking course so simple. For those who are new to machine learning and thinking of doing this course, please do it without any hesitation. Blessings to all of you, and I wish you all luck for your future projects.

By DL

•Jun 16, 2019

Excellent introductory course on Machine Learning, even for people who don't know a whole lot of linear algebra. One can do this course in two ways: First is to look through all the pre-existing code in the test exercises and really understand how all it works and the second is to only focus on the portions that the course expects you to finish. Just finishing this course would not make you an "expert", but this is a very well structured course to get basic concepts firmly rooted in-place. Since this way my first foray, and I am not a good programmer, I was looking to focus on concepts, which this course truly enabled.

Finally, any good course is as good as the teacher makes it to be. Andrew has done an excellent job in taking the most important aspects and simplifying them into bite size pieces. My gratitude to him for the excellent work he has done for the benefit of learners at large.

By Yichi Z

•Oct 11, 2020

My graduate ml-introduction course professor actually recommend this course. I only focus understanding the exam math and concept at the time. This course gave me a solid refresh and a better general explanation on some of the doubt I can't help to clear before. For the programming assignment, I saw people who give 1 star review complaining that it doesn't teach you how to do it end-to-end. For me, the simplicity and moderate depth actually help me to unveil the mysterious mask of ml, which is a pretty big deal for me. This programming assignment itself essentially just ask you doing one thing, that is vectorize calculation. But that doesn't mean it only teach you that alone. I skip most of the peripheral code. And I think for a start of implementing end-to-end, those peripheral actually give you a good sense of direction what else you will need to go in depth and implement yourself.

By Marcel D S

•Jan 29, 2019

Absolutely fantastic course. Professor Ng's approach to teaching is a bit more top-down, so you'll get a lot of useful theory and intuition about how the different Machien Learning algorithms presented in this course actually work. Do not expect to be terribly fluent in implementing these algorithms after completing this course, though. I found that the programming exercises really show their age and although I recognize that Matlab/Octave is a nice programming language to implement matrix operations, I still feel that having to use this language is the weakest part of this course. But there is no single course that will teach you everything you need to know about Machine Learning. Considering the huge amount of intuition I've gained, I feel like this "11 week" long journey (it certainly took me longer than that!) was one of the most elevating educational experiences I've had to date.

By Mark A S

•Jun 8, 2018

This class is a basic course in Machine Learning - "basic" as in foundational, not as in "too simple": "basic" as Linear algebra and partial differentiation are "basic"; however deep knowledge of these aren't required for the course. Dr Ng is a superb teacher, and provides everything needed for even novice programmers to acquire a ML foundation. Before taking this, I started watching his older Stanford CS229 videos (still available) and they too are excellent - covering some topics more in-depth, but they did not come with graded tests or programming exercises. Half way through I started a Hinton Coursera course in parallel and quickly exited; I found this course much clearer & more accessible, and the programming work I found to be both rewarding and reusable. After each week I had an intense desire to actually find a relevant real-world problem and apply the current lesson to it.

By Alexander W

•Jun 16, 2020

I give my highest recommendation for this course. Among my other courses within mathematics, statistics and computer science this will be my number one course before all others. Since I grew up, I have admire the universities in USA and this course is a prove by that. Thanks to Professor Andrew Ng and the mentors, I have managed to complete the course within Machine Learning and gain more insight of computer science. After this 11 weeks, I believe, I will mainly remember the course moments of Neural networks, the applications of Logistic regression and all hard work for vectorized implementation in Octave to complete the programming assignments. Besides this and that the course was given online, I will also associate that this course was given during the time of World Pandemic. Once again, Thank you for all advices and the excellent course.

Sincerely

Alexander - Sweden

By Jon I

•May 15, 2017

This was a very enjoyable introduction to and overview of machine learning, from the perspective of someone who doesn't want all of the detailed mathematical justification for the machine learning algorithms, but does want to see the nuts and bolts of how they are implemented. For example, we don't prove that we have the partial derivatives of the cost functions (or, even, really justify what partial derivatives *are*), but we do see how they are used as the input to gradient descent routines which let us optimise systems such as linear regression. It is an ideal course to use as a springboard for different aspects of machine learning, such as the Neural Networks course which is also on Coursera.

The main lecturer is clear, and does a very good job of explaining some of the practical aspects of machine learning, and when you might choose the different optimisation techniques.

By Dr. H d l H G

•Jul 1, 2020

I am impressed by the tender and empathic way that Andrew uses to explain the material. It is a pleasure to listen to him and learn with him! In addition I am very thankful to the people that put continuous effort to improve the programming exercises and answer the questions of the students, so that we can walk through the exercises focusing on the main concepts of the course - you all made a wonderful job!

The only thing I would suggest is that you find a way to offer lower prices to students in low-income families. 80 dollars may be perfectly affordable for many Europeans like myself but it would be a lot of money for very capable students in other situations in the world.

Apart from that it is almost unbelievable that these algorithms (videos and code) are offered for free to the whole world over the internet - I feel very lucky that someone recommended this course to me!

By Cameron F

•Apr 1, 2018

I graduated from college last year with a math degree without a ton of programming experience; I'm working as a developer now and I've gained a lot of confidence with my programming ability, but I hadn't quite found a good way to satisfy my curiosity about machine learning algorithms. This course was taught incredibly well--relatively accessible while being comprehensive enough to satisfy my curiosity in situations where I wanted a bit of a look behind the curtain at how these things really worked (which is the issue with many articles/tutorials online--either they plop down equations without giving you a shred of intuition about how they work or they go right into the weeds). The workload was of course lighter than it would be in a typical college course, but honestly I was very pleasantly surprised by how much I learned and was able to retain in this course. Thank you!

By Praveen S

•May 26, 2020

Andrew NG's style of teaching is so charming and all the lectures are extremely well thought out and structured. The course load per week was also perfectly designed in terms of the time it took. The quizzes and the programming assignments also gave a lot of insight on how to program machine learning applications. This course is perfect for beginners in machine learning. Andrew NG teaches everything from the start and doesn't dwell to much on advanced math and covers the requisite math pretty well (but for those who want to go in depth, he offers an insight into the concept behind it so that you can google it for yourself). If you are interested in data science and looking to start a career in the same, this course is for you. Thank you Andrew for introducing the vast and wonderful world of machine learning and data science to me and countless people all over the world!

By Guru S T

•Jun 24, 2017

I thoroughly enjoyed Dr. Andrew Ng's lectures. He mastered the art of teaching complicated concepts (stat and linear algebra stuff, ML is notorious for) in a simple and clear way. I loved the graphs, 3d visualizations throughout the course material. I specifically loved the real-life neural networks example (a simplified version of self driving car video) shown in one of the lectures. This course is designed to equip students with not only the foundations of machine learning but to ignite a genuine interest and love for the field, which is a really important goal for any course. The programming assignments, review materials and discussion forums, all provide tremendous learning around the concepts taught in lectures. I would suggest every student taking the course to thoroughly understand course materials even at the cost of extending your course session to a later one.

By L'EMIR O C

•Jul 19, 2017

Wonderful course! Andrew Ng has a way of helping you grasp the intuition of Machine Learning material whilst giving substantial leads into the more technical aspects. His vocabulary appeals both to the aspiring statistician and the amateur enthusiast. One ends with a thorough picture of the subject, from motivations of a Machine Learning problem, to the mathematics of regression & neural networks, to algorithmic implementation with good habits (clean code, vectorization, parallelization, etc.), to practical advice on error analysis & effort distribution. The 'coding' homework is gratifying : for pedagogical bits of code entered, we are rewarded with a graphical display of Machine Learning at work. Furthermore, every technical concept is introduced or illustrated by a clear, contemporary real-life situation or simple 2D example that sticks with you. Thank you Andrew Ng!

By Herman A

•May 12, 2016

Taking this class was quite a challenge as I took many different classes on different platforms in preparation for my going to grad school.

Professor Ng's class is, pedagogically, a very masterfully planned class. His simple but meaningful way of teaching made digestion of new information easy. He also used media to aid in teaching in an expert way, like using different colors to help keep track of variables, and using clear and meaningful diagrams.

And also, in behalf of the TAs and mentors, I'd like Professor Ng to know that they are a great asset. Without their help, most notably in the form of programming assignment tutorials and test cases, this class would have been much harder. Their step by step guidance to those of us unfamiliar with R or computer programming was essential, all while upholding the code of conduct. The TAs and professor Ng make a formidable team.

By Steve H

•Sep 14, 2019

Beautifully organized overview of basic concepts and applications in machine learning. The instructor is skilled at communicating an intuitive understanding of the algorithms and the essentials of how they work. He covers the key ideas while avoiding getting bogged down into mathematical and technical complexities. He provides diverse examples showing how the ideas can be applied to big datasets, images, spam filtering, robotics, website traffic, etc. The course is enjoyable and entertaining.

Most of the math is at the linear algebra level, with occasional mention of calculus. Students who are very comfortable with Matlab/Octave and linear algebra will have no trouble following the course and completing the programming assignments. Students who are unfamiliar with linear algebra will probably struggle and should brush up on those skills before starting the course.

By Ahmed A

•Sep 13, 2018

The course was extremely beneficial for me.

The tempo of the classes was optimal. Not super fast that you miss details and also not super slow that you lose attention from boredom.

The content is very well selected. In a nutshell a student can get a very good broad view on various topics. I would call it "To the point".

The programing excercises were the best thing because it gave me an opportunity to apply what I theoritically understood and play with the scripts a bit with debugging mode to deeply understand the concepts being executed in front of me. That helped me verifying my understanding and made it hard to forget what I learned.

I have to admit that some of the content I already read about before but it wasn't until going through the course that I started to deeply understand what I read before. You know I had a lot of "Aaaaah okay that explains how it works" :-)

By Heiko S

•Nov 14, 2016

Andrew, a big 'thank you' for teaching this class! Even though I DON'T consider me as an (real) expert in ML now, I got a thorough understanding of many many topics of ML. Prepared with an arsenal of algorithms and a lot of great advice I feel ready for take-off. The course has been teached in a perfect order, explaining not only the mathematical underpinnings (luckily not in depth) but focusing on the intuitions, rationale and use-cases behind the different algorithms. The many given examples (lecture and ready-to-run-code in exercise) have been very motivating and inspiring.

Some exercises turned out to be a bit time-consuming when trying to implement them in vectorized form. The catchiest part has been installing Octave (4.0.3) on OSX. I strongly recommend to go with the virtualbox/vagrant/xquartz based installation (see resources). So that's it. Thank you again!

By saurabh k

•Jan 26, 2017

I am Wondering Before I Don't know how to Implement Recommendation Features and Suggest Products On Home Page as Much Targeting the User We Have done Lots of Effort and Design More Complex Database tables and Structure but i but we did not Mining The Correct Data as per Users Views and Choice Thanks To This Course it Help me more and more in Redefining my Programming Technique and Think To Move through the Artificial Neural Networks and NLP For Getting More Relevant Search dataset collection For Targeting More and more users and thank to you also Coursera team and Respected Prof. Andrew ng He is Delivering an very Nice Tutorial Session and Bunch of Assignments which is actual relevant and Genuine Example which are Understandable to any new student who Join this course and Thank you for this course forum moderator also they are really nice .

Thank You

Saurabh Kashyap

By Guillermo A

•Oct 7, 2018

The course is really well structured and easy to follow if you have basic knowledge in any language programming language. Basic concepts of probability and calculus might also be handy for understanding some of the proofs or derivations, but are not needed at all to follow the course. I particularly liked how you get to implement some ML algorithms yourself and see them in action. It is also worth mentioning that the programming exercises just prompt you to complete the key parts for each assignment, so you don't have to code everything from scratch. For example, for the problems, you are usually given the functions to load and visualize the results, and your task is to implement just the logic for the task. One thing that could be improved is that after week 6-7, there are no lecture summaries after each video, which I found very helpful during the first weeks.

By Aleksandar I

•Jun 18, 2018

I am primarily working as a PhD student in Neuroscience. I came to this course with mostly biology background and some decent, although not extensive knowledge in maths and stats. The course has been absolutely fantastic. All the topics were very well introduced and developed. I was really astonished and pleased how well Andrew Ng was able to explain the concepts clearly and comprehensively, without sacrificing rigour or depth. He goes into enough detail in the math to give you a thorough theoretical grounding without assuming tons of knowledge and for the more advanced students points out relevant topics they could explore further if need be. I think this is an excellent introduction to machine learning and I would highly recommend it to any student in Computational Neuroscience/Biology or anybody really who wants to know more about ML. Andrew Ng is the best!

By Dante K

•Aug 24, 2020

"Why should I learn programming? What is it useful for?". This, this is what it is useful for, regardless of the area you're working on. While I don't agree with Octave/MATLAB being the most beginner-friendly platform, when Python is the prefered language for machine learning, Andrew Ng succeeds in giving a great overview of most areas of machine learning and how easy it can be to begin playing around with it. He goes on not only to explain how to use these algorithms, but also teaches the important metrics which, while not fun or glamorous to talk about, can save you tons of money and time and allow you to get much better performance in your applications. Overall, I'll reccommend this course to absolutely everyone who wants to learn Machine Learning, even to people who don't know how to program and were asking themselves "Why learn and where should I start?"

By Andrew W

•Apr 23, 2020

Extremely enjoyable course. Professor Ng really takes the content down to an understandable level by focussing on conceptual ideas over the mathematics (which is still interesting, though would probably be better learnt independently or at universities). As a maths student myself, I got a lot of value from this course's introduction to all the main algorithms used in machine learning and how to best apply these tools. Although the course is quite introductory in terms of difficulty of the maths/programming exercises, it is very comprehensive and well worth the time for any beginner with a decent mathematical background (some knowledge of 1st year university maths). I also liked how MATLAB was used to simplify the programming and it was obvious a lot of effort had gone into writing the MATLAB guide scripts. Would definitely recommend this course to my friends.

- Finding Purpose & Meaning in Life
- Understanding Medical Research
- Japanese for Beginners
- Introduction to Cloud Computing
- Foundations of Mindfulness
- Fundamentals of Finance
- Machine Learning
- Machine Learning Using Sas Viya
- The Science of Well Being
- Covid-19 Contact Tracing
- AI for Everyone
- Financial Markets
- Introduction to Psychology
- Getting Started with AWS
- International Marketing
- C++
- Predictive Analytics & Data Mining
- UCSD Learning How to Learn
- Michigan Programming for Everybody
- JHU R Programming
- Google CBRS CPI Training

- Natural Language Processing (NLP)
- AI for Medicine
- Good with Words: Writing & Editing
- Infections Disease Modeling
- The Pronounciation of American English
- Software Testing Automation
- Deep Learning
- Python for Everybody
- Data Science
- Business Foundations
- Excel Skills for Business
- Data Science with Python
- Finance for Everyone
- Communication Skills for Engineers
- Sales Training
- Career Brand Management
- Wharton Business Analytics
- Penn Positive Psychology
- Washington Machine Learning
- CalArts Graphic Design

- Professional Certificates
- MasterTrack Certificates
- Google IT Support
- IBM Data Science
- Google Cloud Data Engineering
- IBM Applied AI
- Google Cloud Architecture
- IBM Cybersecurity Analyst
- Google IT Automation with Python
- IBM z/OS Mainframe Practitioner
- UCI Applied Project Management
- Instructional Design Certificate
- Construction Engineering and Management Certificate
- Big Data Certificate
- Machine Learning for Analytics Certificate
- Innovation Management & Entrepreneurship Certificate
- Sustainabaility and Development Certificate
- Social Work Certificate
- AI and Machine Learning Certificate
- Spatial Data Analysis and Visualization Certificate

- Computer Science Degrees
- Business Degrees
- Public Health Degrees
- Data Science Degrees
- Bachelor's Degrees
- Bachelor of Computer Science
- MS Electrical Engineering
- Bachelor Completion Degree
- MS Management
- MS Computer Science
- MPH
- Accounting Master's Degree
- MCIT
- MBA Online
- Master of Applied Data Science
- Global MBA
- Master's of Innovation & Entrepreneurship
- MCS Data Science
- Master's in Computer Science
- Master's in Public Health