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Voltar para Aprendizagem Automática

Comentários e feedback de alunos de Aprendizagem Automática da instituição Universidade de Stanford

163,453 classificações
41,933 avaliações

Sobre o curso

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....

Melhores avaliações

30 de Ago de 2020

A brilliant sequence of topics and fundamentals to get a stronghold on ML . The learnings I obtained from this course will always be my guiding factor in working through the projects in my life ahead.

18 de Jul de 2021

Amazing really felt that I learnt something substantial. Very happy that I chose this course over others Andrew Ng Sir explained everything very clearly to a required level of depth.\n\nThank you Sir!

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401 — 425 de 10,000 Avaliações para o Aprendizagem Automática

por Adarsh K

24 de Mai de 2019

The best Introductory Course on ML ever. No Pre-requisites allows anyone with the interest to learn ML learn it in the best possible manner. The course not only gives the Theory but also develops Intuition behind every algorithm which helps to retain the essence of the entire material. Not only the Theory but also the Practical Advices that the prof gives helps you to implement a ML Project from Scratch and Diagnose any possible error that may creep in, some of which aren't even used by many Industry Professionals. The prof is very humble and teaches you more like a friend, giving examples on how simple things may go wrong, also accepting that some of the concepts are not so easy to digest, so don't worry. The course is superbly organised which helps learners learn everything that the instructor wants to teach. The Quizzes, in-Lecture questions, Programming Exercises enable you to step through a path of- learning the theory, building the intuition, getting practical advices, implementing the code and inspiring you to work on your own projects. The Discussion Forum is always very active, you could clear any and every doubt of yours. Thanks and Congrats Prof. Andrew Ng on making the best MOOC ever!!

por Hooman R

17 de Jun de 2017

Excellent course. You will learn linear and logistic regression, SVM, neural networks and many more algorithms (supervised and unsupervised) You will also learn about how to evaluate the algorithms and how to design a more efficient system.

The teacher knows well how to teach the concepts in a way that you will gain a deep understanding rather than just memorizing formulas.

The quizzes are brilliantly designed to make sure you have learned the material entirely.

The computer assignments are created with profound details and you will do them in Octave or Matlab. Implementing the algorithms will help you fully understand what is happening in these algorithms. Enormous work has been done creating these assignments. Hats off to the designer of them.

The only thing that I would say could be better about this course, is the SVM topic. Unlike the other algorithms described in this course, the SVM algorithm has been explained in less detail than I expected (even with watching the optional videos). What I mean is, in order to gain a deep understanding of the SVM, one would need to see other sources as well. However, for any practical purposes, this algorithm has been explained well enough in this course.

por Chia-Yu C

24 de Fev de 2020

Professor Ng definitely is gifted in the sense of turning complicated concepts into reachable, comprehendible ideas, which is the best thing I can expect when entering this complicated ML world.

To me, this class is more application-oriented rather than theory/math solid. Surely there are pros and the cons of doing that. One obvious advantage would be, even with limited math (mainly linear algebra and calculus) knowledge, ones can still have a great time playing with the ideas and models in ML field. The course definitely serves as a terrific introduction to this field. Andrew had all the ideas well-covered and made sure that after this class, students can apply these to real-world without bumping into some big troubles.

I don't think the lack of math proof or formula deduction can be fairly stated as the cons of this class. Though ones will definitely need to familiar those if they have serious ML jobs to be done, this class still serves as a great starting point to help people navigate which ideas/theories to dig deeper into.

To conclude, I highly recommend this class and encourage people who are looking for building more solid math foundations can find extra readings along the course :)

por Jeremy F

8 de Ago de 2017

Excellent course. It has an easily understandable introduction and keeps gaining speed and complexity as you go on. While the first quizzes and assignments are quite easy, the later ones (except the final chapter) become a real challenge. I had to repeat some of them a few times. The additional resources are absolutely helpful, it's a shame I didn't use them until week 6 or 7.

I think by the end of the course everyone should understand what machine learning can do and what not, beautifully supported by real life examples. Until week 10 this course seriously left me wondering how machine learning is applied at all in a real-life work environment, but chapter 11 cleared things up.

The final chapter, as a whole, is the one where you know you've done the hard part. It's basically one big example to illustrate how you can apply your current knowledge on machine learning. It's your reward for all the patient learning.

You should still be aware that the world of machine learning is huge, and by learning the theory you merely scratch the surface of it if you complete this course. Me, for instance, I will move on to other courses to deepen my newly acquired skills, or to get some practical experience.


8 de Fev de 2019

Exceptional. Andrew Ng brought a lot of himself in this class. He is a master of teaching complex topics in simple ways. I have learnt a lot from his teaching skill, in particularly on how to transform complex concepts into simple statements which is quite relevant in my job today. Not everyone is an engineer and yet many people around us have heard about ML. But many misconceptions are said. This class will help you make your message crystal clear. A big community has been growing up all those years and he deserves it.

I have started taking the class many years ago for free but had not the time to finish it because of a busy life as many of elearners here. Now it is done and I have paid for it without any regret. As a ML engineer, I had especially an eye into his ability to communicate complex concepts in simple way to his students. If you are a quantitative engineer, you may pass all 100% quite easily. But what matters here is not the hardness of the questions, it is all about listening to a talented teacher. I must say he masters the communication. He is an exceptional professor, a reference to teach online courses. I have taken many MOOCS for the past 5 years. This is easily a A++.

por Subramaniam S

21 de Jul de 2017

Wow! What I can say! Thoroughly enjoyed a computer science subject with plenty of Mathematics. And, that is at the age of 51. I enjoyed going through each of the Video and the subsequent notes and quizzes. The quizzes took lot of time and needed reviewing the materials again, for most quizzes. I initially struggled with programming exercises. The speed and my familiarity with matrix multiplication increased exponentially and finally finished the last few programming exercises in no time.

For an average Joe, I will recommend this course to take at a leisurely pace, referring to several materials outside. I too was reading couple of books while going through this course. The books really emphasized the learning. A couple of books most suitable to read along with this course are, Machine Learning by Tom Mitchell and Introduction to Machine Learning by Ethem Alpaydin.

This course improved my confidence tremendously as I was not programming hands-on for the past several years. I have not even used any IDE as most my programming experience was on Unix machines using vi editor. This course made a swift change in my thinking and imbibed confidence that I can code complex systems.

por Jian X L

31 de Mai de 2021

The course on Machine Learning given by Andrew Ng has been a nice learning experience and a huge professional step toward my career objectives. I have appreciated the didactic and detailed description of the different concepts and insightful examples. Without going too deep into the mathematical development, Andrew ensured that the lectures are easy to understand.

However, if I had to assign one bad mark, it would be related to the programming exercises. Actually, I have had the feeling that there are too many hints and indications; e.g., the instructions in the pdf file say which formulas must be implemented in a given part of the code, all the pieces of code for plotting, optimizing, etc. were given already.

In practice, I expect that I will have to code a machine learning algorithm (for a given application or problem) from scratch. Yet, as I am not a professional developer, all the clues helped me to get through the exercise quite fast, which was also appreciated.

Finally, the course was time-consuming. Yet, as said by Andrew in his videos, time is a valuable resource that we should dedicate to something that worth it. And Machine Learning is a thing that deserves our time. :)

por Aashirwad

21 de Out de 2017

An amazing course! The lecture videos and slides are well-prepared and the concepts are explained by prof. Andrew in a clear and concise way, using neat graphs and plots when necessary. A lot of effort has gone into making the course largely self-contained. There's more focus on the application and practical implementations of machine learning algorithms than their mathematical and theoretical details (although not at all necessary, a fair exposure to advanced Linear Algebra - derivatives of multivariate functions, matrix decomposition, projections, etc. - can help in understanding some of the algorithms better). Lots of tips and tricks are given to help troubleshoot problems that often occur in practice.

The programming exercises are designed so that the student can focus on understanding the essential topics instead of getting bogged down in too many details (nevertheless, it's a good idea to briefly go through the functions and files already written by the staff). The quizzes are also well-designed and help the student recognize important nuances in the subjects.

There's a lot to be learned by taking this course! Thank you, Professor Andrew Ng and Coursera staff!

por Matthew J

2 de Mai de 2017

A really excellent course.

This is the first online course I've taken, so I cannot compare it to others, either on Coursera or other MOOC platforms, but I can say that it was perfect for my needs and I learnt a lot from it. The math content - of which, obviously, there must be a lot of - is very well-explained, and Andrew takes care to require little more than a high-school level expertise to understand.

The programming exercises were slightly challenging, but not overly so, and helped solidify understanding - and hey, there's always that little thrill of excitement when you see your program begin to give you real answers, and you realise that you've just written a program to recognise letters when given just a bunch of pixels.

I didn't use the forums much during the course myself, but they appeared to be very well-supported by knowledgeable and helpful mentors. (Tom Mosher, in particular, seemed to be on-hand all day, every day!)

As to Andrew Ng, the lecturer, he clearly has a deep and extensive knowledge of his subject matter, but his presentation is always kind, enthusiastic and helpful, so I'd like to pass on my thanks to him for making and presenting this course.

por Simran K

9 de Fev de 2019

For the past few years, all I've been hearing is the word "Machine Learning" being thrown around. In my head, it was built up to be something really difficult that I had no idea about. I wanted to change that, I wanted to be a part of the conversation. This course has truly helped me do that, even as I go through more professional forums about machine learning, I understand the concepts a lot better. It's no longer technical jargon out of my reach.

Andrew Ng really breaks down the course to simpler elements. He brings up layer on layer of abstraction while keeping the students interested with real world application. The presentation, documentation and course assignments are planned perfectly. It's so simple to follow everything, and yet, you still gain more understanding as you move forward.

The community and discussion forums are a great help as well. I'd recommend this course to everyone looking forward to know more about Machine Learning! Don't let the math scare you, it's for your understanding, it's okay if you don't completely follow it. I'd suggest going through an intermediate maths course to relate to it better, but it's okay even if you go without.

por Matteo L

27 de Abr de 2020

An absolutely fantastic experience from start to finish. A great approach to teaching this material and making the student feel like part of the class right away. The contents were incredibly interesting and the structure of the course was absolutely perfect in my opinion. Andrew Ng. is a fantastic teacher and you can clearly see how passionate he is about this field from the get-go.

I think it's also important to mention you can see the hard work put into constructing the exercises and providing structured information in the resources tab and the discussion forums thanks to the mentors.

The only (small) negatives I'd mention maybe are the fact that random forest (or similar) algorithms were not discussed (maybe there is a reason that I am not aware of) and possibly the exercises tended to get a little bit less challenging towards the end. I think an exercise on optimization using the stochastic gradient descend and the mini-batch gradient descent could have been a nice add to the list of exercises as well.

Once again, overall this course really should be considered a reference in terms of teaching and course structure for MOOCs and courses in general.

por Aaron S

8 de Mai de 2021

I think this course is fantastic for anyone who wants to explore Machine Learning. However, it is not perfect. Professor Ng focuses completely on the Algorithms and avoids going too deep into the mathematical aspect of those algorithms. What does that mean for you? For Mathematicians and Computer Scientists, you are left wanting more. All the technical concepts that Professor skips seem deeply familiar and yet a bit too complex to delve into by yourself. For people with no experience in Maths or Programming, this means A TON OF MEMORIZATION. Being familiar with Linear Algebra and programming is key to completing this course smoothly. If you are not familiar or not experienced enough, then you would have to grind all those basic mathematical concepts in your head as you go along with this course, just to keep up with the deadlines. However, I will say this - if you complete this course successfully with flying colors, then you would have gained skills and knowledge that not even some formal University courses can provide you. Hence, I recommend this course 100/100, but I would rate it ~4.7/5 just because of how it sort of flies over everything.

por Federico L B

9 de Ago de 2020

This course was absolutely phenomenal. The main teacher Andrew NG made all the videos and classes so fast and seamless to watch with very interesting examples of real life, as well with important theoretical explanations. The topics, additional contents, extensions and real life cases were delightful to learn. The reviews at the end of each video made for a very fun and interactive way of demonstrating what you just learned in a video class. The reviews at the end of each sections were difficult at times but fair and rewarding when passed. The exercises were very difficult at times but the amount of resources, help from the mentors and the community and the general support to the students was more than enough to help me obtain a 100/100 score on each one of them. The only issue I would have is that the last few exercises were very difficult to understand. This meant that the code did almost everything and I felt like I did very little and that I myself could have not done what the code was showing me. But maybe it is tuned as close to perfection as it is. I can only say thank you and I really hope this helps me find a job as a Data Scientist.

por Kunind S

31 de Jul de 2020

A really amazing course by Prof. Andrew Ng. He covered all the majorly used Supervised and Unsupervised Learning Algorithms. Now these things are covered by many other courses too, so what's so good about this course? The answer is Prof. Ng's lucid ad easy to follow explanations so much so that, you don't really need to be a wizard or even have a knowledge of College level Linear Algebra or Vector Calculus. Also what additional information we gain from this course is not just the Theory behind ML algorithms BUT also the practical implications! These things are crucial since most of us aspire to be ML Engineers in the applied space. He teaches us ways to debug our algorithms, practices which are industry relevant also how to improve performance of our algorithm, what to devote more time and energy resources to, etc. Just be committed to this course, you'll start loving ML and getting a hang of it in no time! Well it would have been "another" cherry on the cake if Prof. Ng had included other algorithms such as KNN, Naive Bayes, Decision Trees, Random Forests and a much deeper implementation of SVMs, but overall, I'll rate the course a 5/5!

por Mandaar P

25 de Jul de 2019

An excellent course. Very well structured and well paced. The quizzes and problems in every week have been extremely well thought of and provide a very good insight into the concepts explained in that week. The barrier of 80% for clearing each quiz and each week's problems is very good and important.

Andrew NG is a very likable person and obviously comes with fantastic experience in the area of AI/DL/ML. There is one suggestion though. It is important for everyone taking this course to have a good understanding of linear algebra. So while Andrew does explain the mathematical concepts of each of the algorithms quite well, I believe he should not underplay the need for understanding that math even though some concepts are advanced. It is certainly important that everyone who takes the course, realizes that it is not just using an algorithm, but that the mathematical foundations underpinning the algorithm are equally important.

All in all I thoroughly enjoyed the course and will be taking up the Deep Learning and AI courses eventually, which Andrew has already developed.

Hats off to Andrew and team for a wonderful learning experience.

por Debangshu M

9 de Jun de 2017

I am only 4 weeks in this course now. I am loving it!!

I must say, this course if very informative. I like the content, which is very precise yet easy to grasp. The course gives enough fundamentals, yet leave some of the finishing work, which is necessary to solve a particular problem, to be done by the students. For example I enjoyed thoroughly determining vectorized representation of the algorithms. Coming from High Level programming languages (I am a .NET developer), I had to unlearn easy way of implementing (For loops) and learn the new (and fun!) way of vectorized solution of Cost Function, Gradient Decent, Logistic Regression etc. Also I had to brush up some knowledge on calculus and matrix algebra from college days. Those are necessary to truly understand the beauty of these algorithm and working out an elegant vectorized solution.

Last but not the least, this is my 3rd Coursera course. This course provides me familiar experience, ease of using the platform, with all the great new knowledge in a concise format. I would like to express my gratitude to the trainer for a great learning experience and such an outstanding course.

por Piyush B

23 de Abr de 2020

The best thing about this course is that it takes you step by step into the world of machine learning without overwhelming you. The initiation is simple and the complexity builds with each day and week passing by. So when you look at the content you feel intimidated but once you get down to take a day/week at a time it actually unfolds pretty well.

One more thing which makes this course great is the practical wisdom which Andrew provides. Given his vast experience in this area, he is able to explain the pitfalls, the thumb rules, the way to move ahead without getting lost. He is able to connect the dots, provide real life examples and also explains what lies beyond.

The other great thing was assignments which have been designed very well with starter code. You really need to do only the core algorithm implementation but running the completed code almost gave the feeling of implementing a mini project instead of just writing some code snippets. This helped in seeing the code execute from end to end with data visualization, predictions to measuring the efficiency of the algorithm.

Thank you for this course. I thoroughly enjoyed it.

por Peter L

2 de Abr de 2019

This course is perfect if you are a beginner in Machine Learning and would like to get some gentle yet thorough exposure to the field.

Professor Ng is an enthusiastic teacher who presents the material in a very accessible fashion. He doesn't get too deep into mathematics but teaches you enough to get a sense for what exactly a learning algorithm is doing under the hood.

Some minor criticisms: The programming exercises each require you to complete some predefined functions with a couple of lines of code which, given the extensive instructions, is often trivial - here I would have wished for a steeper learning curve. Furthermore, I would have liked to hear about additional topics such as Decision Trees, Ensemble Learning and perhaps more about the different types of neural networks.

Nevertheless, I warmly recommend this course to anyone interested in Machine Learning. You'll walk away with a deep understanding of several key algorithms, some experience in how to implement them, some knowledge about real-world ML applications as well as a number of very useful guidelines for data preparation, model selection and error analysis.

por Alan J R

21 de Mar de 2020

If machine learning is interesting to you then I would surely recommend this course. Professor Andrew Ng really makes it understandable and easy to grasp, honestly. I come from an economics and finance background, so I had some prior knowledge on linear and logistic regression, but I could easily see myself still understanding these topics and the whole course if I had not studied economics and finance previously.

Also, I learned how to use MATLAB which I consider a very valuable skill. At first I was overwhelmed by the software and how to use it and I tried to run into it head first. However, I recommend taking it slowly at the beginning and really relying on the discussion forums, because everything is there and it is a super active environment. Here I would like to thank Tom Mosher as well, because his contribution to answering questions on the discussion forum resulted in me not having to ask any questions. This course is quite old, but it is also ripe, because so many people have done it before and you can find answers to almost all of your questions. Again, really big thank you to professor Andrew Ng and Tom Mosher.

por Methus P

28 de Jun de 2020

This course is one of the best courses I've ever taken, both online and in real life. This course requires no prior knowledge, meaning that anyone who has an interest in computer science, or particular, Artificial intelligence, can finish this course. I love how the programming assignment was designed and how such great so-called classmates have helped each other along the way. The mentors are very supportive. Before I started this course, I have no idea what machine learning is all about and what it can do. Then prof. Andrew Ng just made it looked so simple that I wanted to write the whole program by myself! The contents of this course are well-selected, not too easy, not too difficult, and of most importance, useful for everyone. I'm currently studying Medicine (I'm interested in BOTH Computer Science and Medicine, but I thought CS could be studied online) and found many potentials in improving the world's healthcare. I never regret spending my time finishing this course.

Conclusion: Highly recommended. You don't need to major in Computer Science to learn this course. It definitely will be useful in any field.

por Jose A G

3 de Jan de 2018

Awesome class. I took it while also taking Data science and Machine learning at my school. I felt like it was very informative and actually explained a-lot of material better than my school teachers. I like how Ng went above and beyond to not only explain what are the different types of machine learning algorithms available, but also tips and tricks on how to properly use them and also explain industry insight into these problems. The difficulty for me was not too hard, there are many hints sprinkled around some of the assignments, and I like how clear and easy Ng explains the material, and he makes the effort to explain things from the ground up and sets up reminders, which i think is very important. I recommend taking this class as a basis for machine learning, however more study is required to learn about more advance topics in machine learning such as Deep Learning algorithms: LSTM, Generative adversarial neural nets, convolutional neural nets, etc. Take a look at this course's syllabus for a list of topics that are covered and plan your courses towards the complete set of what you want to learn.

por Rick T

2 de Jun de 2018

This is the best college course I have ever taken! I have a MA in Psychology with emphasis on Statistics and Research Methodology and ABD (All But Dissertation) for a doctoral degree, and this class was better than any class I have ever taken. The lecture videos were organized, always on subject and extremely well done. I used to nearly fall asleep in some of my graduate seminars, but had no such problems watching Andrew's lectures. I especially appreciated the karaoke-like presentation of the videos + transcription. I have always done better when having textbooks to go to and take notes. With this approach, I was able to better process the information presented to me. The programming assignments were challenging but not impossible, and the tutorials for each assignments always seemed to provide the necessary clues to find the solution. And on completing the class, I feel that I have gained a significant amount of knowledge of Machine Learning, which provides me a bridge into a new knowledge domain. I highly recommend this class to anyone wishing to learn the basics of Machine Learning.

por Sarang D

17 de Out de 2020

An excellent starter course if you want to start building your own ML systems and have a background in math. The course covers the high-level agenda and issues of developing and deploying ML systems in real life, while disbursing an engaging learning experience with a good amount of math and algorithms involved. The programming exercises are very well setup, helping you focus on the core learning for the segment. (Of course, setting up the problems is also a key part of the ML workflow, and you should try to spend time trying to do it yourself.) Centred around GNU Octave or Matlab, the course doesn't cover the applied aspects of real-time ML systems and deployments for server-grade operations, but it does touch upon the logic behind the MapReduce programming model for big data computing. Overall, the course was an excellent experience - challenging at times (rightly so), but fun throughout. Take this course in its entirety if you are looking to develop ML models as an engineer, and especially if you're looking to get up to speed with ML development as a part of product/portfolio management.

por Vincent D

27 de Nov de 2016

Great class. Much better than most I have attended in person. Excellent instruction, excellent resources, excellent programming exercises, excellent support in the forums, especially by Tom Mosher. Video is a much better medium than live lectures because of the flexibility, shorter segments, ability to stop and study something before going on, and ability to repeat when necessary. Great practice in vectorization. Excellent introductions to the necessary elements of ancillary topics. Bought the certificate. We live in a golden age for learning. Getting this kind of instruction would not have been possible for someone in my situation 30 years ago. I am grateful and looking forward to whatever I learn next.

Took this course to develop skills to work on artificial intelligence and other projects. One previous project described in article at

Very satisfied. I have not been able to stop talking about how good this class is since I began taking it, and will continue to recommend it as the first step for anyone serious about the topic.