One of the best course at Coursera, the content are very well versed, assignments and quiz are quite challenging and good, Andrew is one of the best guide we could have in our side.\n\nThanks Coursera
The course will give you the incites to understand the data driven mathematical functions to write softwares that can behave or change its behavior, based on stimulus (data).\n\nAndrew Ng is excellent
By William Z•
This is an excellent course by Prof. Andrew Ng. Learning from of the best in the industry has been truly an eye opening experience for me. Having a background with some level of software development experience, I have chosen to go with this course in particular (out of the many other courses that's available on the web) because I was motivated to not only understand how to use machine learning tools, but to get a concrete grasp of the theory behind machine learning algorithms, including concepts and intuitions. Short of going back to Uni to get this experience, I know there was a good chance Prof. Ng. would provide a similar academic experience in the course he provided.
An added bonus is that Prof. Ng also would provide advice and suggestion based on his own industry experiences leading engineering teams at world renowned internet companies. This reminded me a lot about the great academic teachers that I have had in my former years of university education (which I found to be invaluable). The landscape of machine learning is rapidly changing and evolving.
I feel like this course provided a solid foundation that grounded many fundamental concepts and motivations of machine learning in a very digestible way for its students. I would highly recommend this course to anyone interested in machine learning who not only wants to use the tools (as there are many guides out there already), but also wants to understand the deeper insights into these kind of technology.
By Sergey G•
Great hands on exercises and very clearly explained material. Was a bit slow for me I had to watch it at 2x: perhaps the simplest maths should be factored out into a separate mini course and assume a certain background for this one. The course is rather broad, though I was surprised not to hear once about Bayes or Markov (n-grams, HMM etc.). It might be a good idea to create a specialisation consisting of a separate basic maths part, all the methods presented here, methods applicable to bioinformatics and NLP too. And to top it all of Computer Vision. I assume the by-pixel techniques used in this course were just illustrating the points, as I would expect Gabor wavelets or something to reduce dimensionality and save ourselves from sliding windows (and rotations as a bonus). I am not sure if in this specialisation I would have liked to have all "science" points (how to run an experiment analyse results) separate from "how to implement an algorithm" and "why the algorithm works" or mixed in as this course does. I think either works. Some navigational infrastructure on coursera would be awesome (wiki style opportunities to jump around between "aspects" etc.). Finally, some summary notes would be very useful. When I do decide to implement any of this I will have to look through the exercise pdfs which are a bit long and at my code - perhaps, at the end, when you know someone has completed the exercises. Otherwise, the exercises are awesome.
By Sotiria K•
This class taught me a lot of the nuts and bolts of machine learning, and by the end of it, I am much more confident in building machine learning algorithms, or joining a team in doing so. The instructor did an excellent job of explaining things slowly enough and in bite-sizes. The programming assignments were very tough (especially because I have very little knowledge of programming languages and Matlab) but very valuable in the end!
A couple of things I did wish for were: 1) A module or part of a module talking about bias of input data. I've heard a lot of about the effects of bias in data and how that can affect your machine learning algorithm output a lot and I wish the instructor told us his perspective on this. 2) Even though I probably would have dreaded how tough this would be, I still think it would be a huge value if we had a real life machine learning project we had to work on towards the end of the course from start to finish, from a fictitous client like Amazon or SalesForce etc. 3) I read about how machine learning programmer interns wrote about their experiences at the job and how they were so focused on getting the algorithm performance high but a lot of their job revolved around understanding the industry they were working in and what their company needed, because a perfect algorithm that has no value for the company is useless. So, I wish towards the end the instructor discussed this more to prepare us for a job.
What a great course! I really loved the pedagogical arrangement of this course. Skipping optional but complicated proofs was a smart move for some maybe not so for a select few topics but I realize that they were necessary to make this class as interesting as possible, and interesting it really is, without a doubt.
One little complaint I have is that while some of the readings earlier in the course were a bit redundant, I definitely did yearn for the readings on some important topics of SVM, k-Means, PCA and recommender systems.
Also another very small gripe of mine is that a lot of the programming exercises had a lot of boilerplate. That definitely was a good thing for smaller things but I still feel like some more work on our part in putting together the exercise as a whole vs just plugging in code in predefined sections could help me get a bit more insight. I say this as I found myself rushing to the next week's content immediately after I finish just the required exercises, although that may be an error on my part. The optional exercises definitely do make up for my needs thought without getting in the way of progress which is definitely good.
All in all, an absolutely amazing course. I realize that I can't become an expert just by attending one course but I also believe that I have taken a huge step in the right direction by taking this course. Thank you Professor Andrew and the Coursera Team for this amazing experience.
By Karl N•
An excellent course that provides both a good overview of machine learning technology and practical exercises that help reinforce the technology. I found it a challenging course as it requires a good knowledge of vector and matrix mathematics, Octave/Matlab programming and some mathematical concepts that I've not used to this extent. The work is ably assisted by an excellent group of tutorials and mentors which help ease what was quite a steep learning curve for me. I can highly recommend this course to learn what ML is about - don't let concerns about the level of mathematics or programming stop you from at least attempting this course. You will need enough free time to view the lectures and undertake the programming assignments and the course timings are pretty accurate, although a couple of the programming assignments took longer than expected due mostly to debugging my Octave code (often stupid errors that took me time to find and test).
In conclusion the course is an excellent balance of theory and practical work to see if you do actually understand what you've learnt in the lectures. Some basic skills in programming and mathematics (especially summation and vector arithmetic) would be of use, but this knowledge is not assumed and you should be able to complete the course and greatly expand your knowledge of machine learning principles, Octave/MATLAB programing and vector arithmetic, all in one course - bargain!
By Tommi J•
Outstanding course on machine learning fundamentals! A wide range of topics is covered in with a very carefully considered balance of understanding how the different algorithms work but at the same time not getting lost in all the mathematical details in the background. Although this means that you will not have seen all the mathematical proofs behind some of the equations that are presented (which would take considerably more time), it also makes for very effective use of your time if you are mainly interested in understanding the intuition of the machine learning algorithms and how to use them. Professor Ng has an incredibly clear and understandable way of explaining complicated topics, and his experience in the field shines through all the way. This course is not content with just throwing a bunch of equations at you but really conveys a very clear intuition about what they really mean, and gives a lot of practical advice on how to troubleshoot your machine learning algorithms, how to prioritise using your time in machine learning algorithm development etc. which is extremely helpful in guiding you on how to actually apply these algorithms to your own problems. The programming assignments are very well designed and will help you get practical confidence in making the algorithms work in practice (also the example applications are very cool and make it fun!) Thank you very much Andrew and all the course staff!
By Tim S•
I should have never hesitated to take this course. It seems to me that anyone who is serious about learning machine learning (outside of a more structured environment such as a university program) absolutely must start with this course. With a tenuous grasp on Python, I am still not ecstatic about this course's use of Octave, but as others have said, one should not be deterred by this. And even though this course does not touch on all of the significant ML methods (e.g., random forests), it definitely delves (a purposefully chosen verb, mind you) into perhaps the most significant. Of note, the transition from one-versus-all logistic regression to neural networks was masterful. And while the dive into neural networks was unexpected for an 'introductory' course on machine learning, it was tremendously gratifying to learn (more than just the basics) about something that has only grown more prominent since the inception of this course. To cut to it, Dr. Ng is clearly a gifted, fantastic instructor. The balance of mathematics in this Coursera version of the course was perfect. I loved learning the mathematical meat of the algorithms and, and the same, *not* having to grapple with unnecessary proofs and the like. I feel deeply privileged to have been able to work through this course. And I am excited that Dr. Ng has now released a new specialization on deep learning (using Python, no less!). Thank you!
By Jianan G•
Great course. At the beginning, the of this course, I just want to learn something about neural network, but then I was fully attracted by this course. My major is biology but Andrew successfully makes me understand every point here. It is logical and understandable. It does not mean that it is an easy course, but reflects the elaborate work and deep understanding of Andrew. Now previous hard fields like computational biology and bioinformatics became quite easy to me.I can easily find out the algorithms they apply and know their shortages. If only I can know machine learning several years ago!
The course covers the underlying mathematical analysis of several famous algorithms like neural network, SVM, PCA and recommendation system. It contains clear instructions to answer 'what', 'why' and 'how' levels of them, and to their actual applications and limits including the workflow to check the quality of my product. It is magic to realize that the advanced technologies like face recognition and auto-driving are just built by such basic blocks.
Learners can have a solid understanding of the different fields in machining learning, and decide whether or not to go further. I proceeded to learn probabilistic graphic model, and hopefully it might be my key figure in my research paper on interfering casual relationship and influence of protein interaction during neural stem cell differentiation
By Robert F D•
First off, I think the course content is amazing! I really like that the instructor used Matlab that encourages the user to create vectorized solutions to the problems. I have heard many negative comments regarding the lack of use of Python, R, or some other library like Cafe or TensorFlow, but I believe all of that should follow after having the mathematical background to understand these principles. The content is not easy, and requires a fair bit of mathematical sophistication, but not so much to lose me, and hard enough to keep me engaged. I really enjoy how each learning unit builds off of the previous one, for instance, linear regression become logistic regression which becomes a neural network.
That being said, I really think that this course needs to have a fresh coat of paint on it. I believe it was filmed in 2008. I don't think the content really has been revamped since its release. The recordings look like they were filmed on an old web cam, not even as good as a modern iPhone. The slides should have some design work on them. I know it seems petty to stress over the presentation, but I think many people are turning to programs from Udacity that are very flashy, but not as technically rigorous mainly, I feel, just because of the presentation. I think that this course deserves a bit of energy polishing it up since it's still perhaps the most popular MOOC course out there.
By Richard H•
Absolutely top notch class - I would say this is the best class, online or off, that I have ever taken:
Instruction is very well structured - building on prior components to build up to more complex advanced ones. This is especially important due to the mathematical and programming concepts required of the domain.
Quizzes are well timed to help evaluate learning - and really the primary purpose is to try to make students think about the subject material and reinforce the concepts. Programming exercises do require some basic knowledge of programming, but the use of Octave (or Matlab) as a tool and the prepared programs in which the student completes carefully chosen/defined missing components definitely reduces the programming burden and helps keep focus on the actual concepts.
The resources for lectures, quizzes, and programming assignments are invaluable, as well as the community built up around those questions. Using them during the course is essential.
Not least of all - the mentors - they are a great help in answering those odd, peripheral questions that really, fully, complete your personal understanding of the material (My personal thanks Tom and Neil for answering my quirky questions)
Kudos to Andrew and the Mentors for an exceptional class - I think it could really make an impact in education and helping realize the huge positive potential of this technology.
By Paradoks S•
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By Jacob M•
Full disclosure: I am a mathematician, and therefore already well-trained in linear algebra, and I'm only 6 weeks into this course.
This course has been a near-perfect introduction to neural networks. Great pedagogical decisions were made to gradually bring the student from basic linear regression to motivating why a neural network is a logical next step to improve this process. This course has made me fluent in important terminology to deep learning and data analysis like bias, variance, precision, recall, and so much more. Not to mention, I've learned MatLab/Octave from scratch, which has turned out to be a nice programming language to add to my collection.
A caveat for non-mathematicians, or for that matter anyone not fluent in linear algebra: the neural networks will be a struggle. Some of the formulas relating to these may also be frustrating because I expect you won't understand why they work, or how to debug your code. I strongly recommend that you consider a rigorous linear algebra course, as a co- or pre-requisite.
To mathematicians: this course is a great starting-off point for learning about neural networks and other machine learning concepts. I already see applications to and from my research. In addition, I am able to explore the literature and decide on avenues for further exploration. Andrew Ng has truly provided a gift here to us.
By Melissa K•
I had always heard great things about this class during my research on what online courses to take next in my path towards becoming a data scientist. I was hesitant because it used Octave, a program I didn't think was going to be useful in my future roles. Then I completely bombed a phone interview in which they asked me the specific steps behind linear regression, and about gradient descent and the cost function. And I realized I didn't really know much about how these algorithms worked, although I used them in my day-to-day projects as a data analyst. So I finally decided to take this class, and after 11 weeks I can say that I experience WAY LESS imposter syndrome than before. I've still got a lot more to learn, but I'm confident that with everything Andrew Ng taught in this course, I will be able to hit the ground running on many other courses, with a thorough understanding of tools to use and the best ways to use them. In the end, Octave was just a little tiny component of a very scope of knowledge you'll attain after finishing this course. So if you're hesitant to take this course because of that, please don't be! I'm really grateful a course like this exists in a way that is easy to understand and is buildable. I'd consider this one of the best uses of my time outside of work, and I'd do it all over again now knowing what I have to gain from it.
This was my first on line course and the experience has been amazing. The lectures by Prof Andrew Ng were clear and the follow up programming exercises helped reinforce and enhance the concepts covered in the lectures.
The entire course was very well organized - videos, notes, discussion groups, suggestions and tips by the mentors was very easy to follow.
The focus of this course is on the practical application of machine learning techniques (supervised learning mostly but also some non supervised learning). Prof Andrew tries not to get into the advanced mathematical concepts but instead provides good intuition and then shows one how to apply the different ideas underpinning machine learning with some practical examples. In my view, this is an excellent way to quickly become familiar the concepts and to see machine learning in action. A student of the course will gain very good insights and can then follow up with the underlying math as needed.
The final two lectures on how to scale machine learning to large systems and a suggested systematic framework for figuring out which aspects of the design to focus on were very instructive.
In summary, i would highly recommend this course to anyone interested in the area of machine learning. Be prepared to work through some challenging (but very worthwhile) programming exercises to get the most out of the course.
It is a very nice introductory course in machine learning. It teaches a good range of machine learning algorithms and good advices on how to implement them effectively. At the end of the course I feel like I am ready to tackle some machine learning projects on my own and I am sure many others who finished will too.
For someone with a strong mathematical background like me (Have a undergraduate degree in maths and theoretical physics), the maths component is lacking. There are little derivations on equations and feels like I could've understood some of the algorithms deeper and instructor cannot teach it here. It feels especially true in supper vector machine. Also if you are strong in linear algebra and really familiar with matrix multiplication rules you can vectorize some implementation very effective and reduce what could be five lines of codes to one.
For someone without a strong mathematical background, I think the main difficulty is going through the messy indexing in equations and actually implementing algorithms with a lots of for-loops. It is also hard to fully understand some concepts behind some algorithms like support vector machine and relate the resulting algorithm with the concept.
I enjoyed the course a lot and really have learnt a lot. Now I feel ready to start some machine learning projects on my own and dive deeper into this field.
By Chin-Chieh, W•
I love this course. It's my highly recommendation to everyone who wants to learn machine learning. Machine learning is definitely the state-of-the-art topic, but it would be difficult to learn because it combines the linear algebra, calculus, programming skills, algorithms, etc. There are tons of threads for learners to get distracted from the main theme they need to focus on. That's why I am amazed by Andrew's machine learning course. It's so amazing that Andrew just makes a very complicated topic very easily understandable and very easily to learn. What's more, whether learners have learned calculus or programming or not, they can still easily understand the mechanism of various machine learning algorithms even though some proofs of the formula are not discussed. Moreover, this course does cover many most well-known machine learning algorithms. I enjoy this course a lot and I am so eager to learn more in the last few lessons. Thank you very much, Andrew. You definitely give me one of the best courses in my life.
By Alejandro O•
Andrew Ng’s ML course is a great introduction to ML. The course covers fundamental learning algorithms in the right amount of depth for the student to gain an intuition for mathematics and applications of the algorithms. While the course can be completed without much knowledge in either Linear Algebra and Calculus, to truly understand the learning algorithms a solid foundation in both is necessary.
The video lectures are succinct and focus on a singular topic which makes it easy to make progress and pinpoint the exact material that needs review. Andrew is honest about what you need to know and provides extensive explanations from problem motivation, intuition, mathematics, and applications.
The assignments are challenging but not overwhelming (except for the Neural Network assignment). The test cases, tutorials, discussion threads, and handouts provided enough support to make assignments enjoyable.
The quizzes are short and the ability to retake the quizzes make them stress free. The questions are derived from the course materials so a quick review of the videos and lectures were sufficient preparation.
By the end of the course, the student will feel comfortable diving deeper into learning algorithms of interest and applying them to projects. I highly recommend this course for those who are new to ML and want to get up to speed quick!
By Ryan C•
Exceptional course. Demanding in terms of time required to complete properly, but worth every second. Future students beware: going through this course is a double-edged sword. You'll suddenly want to solve any problem you have at work/school with some application of ML. If this course is your first comprehensive introduction to ML then expect to utter the phrase "...well now this changes everything" on multiple occasions. Next thing you know you'll be buying books on Amazon trying to learn Python in your spare time so you can REALLY do some ML damage with all the importable libraries.
Structure of the course is perfect. He segments the lectures into a reasonable length, allowing you to bite off a little at a time. There is even the option to roll over into the next class if life throws you a curve-ball. You get the sense that he wants you to succeed and finish the class, and him giving students the chance to extend their enrollment indefinitely supports that assumption.
Take the class. Expense it. Count it towards your annual training budget. Your boss [should] be VERY enthusiastic about you asking for training that costs under $100. It's an easy choice: you can pick 12 weeks of in-depth lectures, notes and hands-on learning OR continue trying to sell your boss on that 2-day, $2500 conference in [whatever city it is this year].
By Matt D V•
Machine Learning from Stanford by Andrew NG is the cornerstone of online courses for Machine Learning - for multiple reasons.
As a non-technical founder looking to further my knowledge and comprehension of the complex AI landscape, AI for Everyone from Andrew felt not enough. Andrew's Machine Learning course finally gets our feet wet with algorithms and the fundamental, underlying mathematics to Machine Learning.
Though intimidating at first, Andrew's Machine Learning class is properly paced, and has the necessary tools and resources to get you through the class without feeling lost. The pacing is adequately supported by quizz that force the learner to conceptualize his learnings to deduct proper reasoning.
Don't kid yourself, this course remains a beginner entry into the AI field and one should expect to take a further dive into more advanced courses of Mathematics, Data Science, Python, Machine Learning & Deep Learning with the ultimate goal to have the ability to create your own ML/DL systems.
The programming assignments in Octave were a bit bleh - as they will unfortunately not translate to production / prototyping environments which are for the most part in Python.
Andrew is a great teacher and makes the complex field of AI feel attainable for driven, smart people from any background.
Best of luck in your journey!
By Cornelis D H•
An approachable introduction I recommend to anyone and everyone that at the least has matrix algebra under their belt. I took this with the intention of reviewing material, yet many concepts I had previously been exposed to were approached in such a clear manner that it felt brand new to me. I'm shocked at how much more clarity this course offered than my university! Moreover, it is absurd that I did a lot more work in my machine learning university course, yet never saw it pay off in an interview. This course was far less work (i.e. less time-consuming), but the pay-off knowledge-wise for interview questions has been demonstrably far far more time-effective. I highly recommend this course if you are limited on time and want a course to give you an edge in interviews. The big downside of this course is that there is no term project. In my case that was fine since I already have ML projects under my belt and needed to review all ML concepts at a general level, but if you don't have any noteworthy ML projects and are looking to dive into one, I'd still highly recommend this course (so many problems I can point out now with my ML projects in interviews for instance!!) but know that you'll want to move onto another project-focused course or a personal ML project after this for sure to get yourself ML-interview ready ;)
By David C•
This is by far the best Coursera (or any MOOC) class I've taken. The production values are low, but the content is excellent. Professor Ng explains everything very clearly. The best part is the homework. Each assignment is explained in detail and the assignments lead you in small steps to make measurable progress as each step is accomplished. The 5-6 page PDF that accompanies each programming assignment provides all the information needed to fully understand what you are being asked to accomplish. I have had MANY other MOOC's where the information in programming assignments amounts to a few sentences embedded in a Jupyter notebook and students are forced to search through numerous forum questions and answers to get an explanation of what you are really being asked to do. Not in this course! Everything is explained very well. I might also mention that while most other machine learning courses are taught at a very high level, using Python and Scikit learn or other ML packages that do the heavy lifting, this course has you actually implement ML programs at a very low level, using linear algebra and vector math so that you really get to see how these higher-level packages implement the detailed algorithms. No black boxes here. I HIGHLY recommend this course to anyone interested in machine learning.
By Ritwik D•
First of all I'm so thankful to Andrew NG for this wonderful course and I'll always be in debt to him for this masterpiece. At the very start, when I was just starting out, I was really unsure whether this would be the right course for me as this is an 11 week course. I was also concerned about the usage of MATLAB/ Octave over Python. But choosing this course, I realized it didn't really matter given that you know how an algorithm is working under the hood. I love how Mr. NG has the special ability to teach such complicated topics with so much ease. I can never expect the same from any faculty I've personally come across. Yes, the programming exercises were sort of a pain but there are github repos that let you do the same in Python. What I've learned over these 11 weeks, I believe, is immensely valuable and probably very few courses out there are capable of that. This perhaps has been one of the best decisions of mine so far. I plan on moving on to deep learning after this, thanks to Mr. Andrew NG.
Also, over this course of 11 weeks I felt attached to Mr. Andrew NG and I can proudly say that he's been the best teacher I've ever come across. It was a little hard to watch the last and final video knowing its the end of the road. So thank you sir for this wonderful work of art! I'll forever be in debt.
By Vincent B•
This is my first online course and I am so happy I selected Andrew's Machine Learning course as my first. The material was well presented, provided plenty of information about why and how you should use each Machine Learning method and importantly he spends time time providing the intuition for why certain mathematical formulas are used. The quizzes were challenging and gave you ongoing feedback on wether you were grasping the material in each section. When you get one of the questions wrong it helpfully points you back to the appropriate material in the lectures to review. Finally, I would like to thank Tom Mosher, a mentor and the author of most all the programming assignment tutorials and a constant presence and help on the discussion boards. I didn't have to ask many questions but that was primarily because someone else already had and Tom provided the answer, often times within minutes or hours. Even though I have little experience with online courses, the amount of support and attention to detail throughout demonstrates how good this course is and I can only hope is held up as a model for other courses. Thank you Andrew and Tom. I am truly grateful and can confidently say it was 10 weeks of work well worth my time and the knowledge that I gained will be used at my employer immediately.
By Cameron P•
This is an excellent course covering the fundamentals of machine learning with a wide breadth of subjects as well as, in my opinion, a reasonable depth into each as well. My only major qualm with the course (and this is just personal preference of what I would like to have learned) is that the programming language used for this course (that you must do the assignments in) is Octave/Matlab. As Dr. Ng mentions in the class, Octave/Matlab is an excellent way to prototype your machine learning algorithms, and after having used it for this course I agree. However, I believe it would have been much more useful (personally) to do the programming parts in a more industry standard language for your final product such as python. Point being, having completed the course I feel that before I can apply this as a final solution I must still learn how to implement these algorithms in a different language with different libraries, so it doesn't feel quite as if my journey is 100% finished. That being said, I do feel that Dr. Ng gave a great mathematical understanding of the algorithms and I do not believe that it will be terribly hard to implement them in another language. I believe the only barrier would be learning the Matrix Algebra and other ML related libraries standardly used in that language.
By Thomas T•
Hi. This is a great course which would be made even better with the small change I describe below. I was referred here by Nicole who was handling this as Support Case #1188060. I understand the mentors have been seeking this improvement for some time. Please let me know if I can clarify or assist in any way. Thanks in advance.
---- Support Case #1188060 ----
Hi. I'm writing regarding the Machine Learning course taught by Andrew Ng of Stanford. I have learned there is a compilation of "errata," known errors in the videos and course materials for each week, which has been made available in per-week links like the following:
I found these links only after digging through discussions. In my own discussions afterwards, I learned that I wasn't the only one having trouble finding the errata. Apparently it's a long-standing problem which creates extra work for mentors as well as slowing down students. Would it be possible to provide the errata links, or a link to the Machine Learning Course wiki, in a dedicated section of the Course Content? This would help a lot of people. Please see the discussion below for more perspective.
Thanks for your consideration.