Voltar para Aprendizagem Automática

4.9

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154,422 classificações

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39,409 avaliações

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....

VB

2 de Out de 2016

Everything is great about this course. Dr. Ng dumbs is it down with the complex math involved. He explained everything clearly, slowly and softly. Now I can say I know something about Machine Learning

QP

24 de Jun de 2018

This course is extremely helpful and understandable for engineers and researchers in the CS field. Many thanks to the prof. Ng Yew Kwang for his great course as well as supporters in the course forum.

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por Cristian B

•2 de Nov de 2020

Sorry to give just 2 stars, but the course lacks effectiveness, big time.

I'm a graduate Engineer, even though I'm new to Machine Learning, however iI find this course way too "university-cut", where the theory lesson is fairly quick and simple and mainly focused on demonstrations and abstract concepts, whiles the passage from theory to hand-on implementation is mainly left to the student, who needs to "figure out" how to do it pretty much by himself.

The aspect where this course is failing is the same where traditional academic tuition is failing, and frankly I refuse to learn things exclusively by browsing tons of questions/answers in forums, cause that's a lot of wasted time. Ineffective.

I'm sorry but I can't go beyond 2 stars indeed, as I really can't proceed with such a dispersive learning path.

por Matthew C

•31 de Mai de 2019

Dr. Yang does an excellent job explaining concepts and showing the detailed mechanics of any example he brings up. This being said, I felt the course offered more of an overview, and for anyone with a college statistics and programming course, this won't be very useful, frankly. The course didn't provide lots of new information, and I think much of the actual theory and implementation for ML and its applications would be better broken up into a series of more rigorous courses. This would however, be a good fit for someone working in management who needs a quick understanding of the most basic principles of ML.

por Ranjit B

•24 de Dez de 2020

While the contents are good and the teaching pace is just right, I am deeply disappointed by the lethargy of Coursera in not fixing trivial errors in its assessment tests. Answers for even some trivial questions are graded as incorrect. Those result in incorrect grading and a frustration. When I am paying to get the assessments and a completion certificate, this is just NOT acceptable!

por Deleted A

•13 de Jun de 2020

Sound clarity is so poor sometime the volume is very low and some point it too hight, how can we concentrate on the course. Online course are stand on two main pillar video and audio, video s good but audio 2/5.

por Anton

•11 de Mai de 2018

Material of this course could be presented much deeper. Mr. Ng tries to avoid mathematical explanations.

por Timothy B

•18 de Jul de 2020

Out of date, and video quality bad enough to be distracting

por Loftur e

•17 de Set de 2018

Assignments are very messy.

por Tibet M

•3 de Jun de 2020

I was quite disappointed in this class where the exercises are too onerous and out of date. For example, Convolutional Neural Networks are not covered. Also, a lot of the material is dated from 6 years ago. There was also no help when I wanted to ask a question. When I asked where a certain material will be covered I did not get any response either. The last 1-2 sections were also wrong as I know that is not what is done in the industry. You will be disappointed if you take this course after a lot of work.

por Maarten d s

•7 de Jan de 2020

the quizzes were very good but the programming tests were badly made and not well enough explained.

some problems can come from having Dutch as first language others from the continuous task of just translating the formula given into a formula for the programming. or just plain old copy paste from the instructions of the file itself

por Miguel C C

•6 de Jul de 2020

Lioso y muy mal organizado. Las preguntas de los test hacen referencia a otros temas y la puntuación es injusta. En general, muy decepcionado y voy a pedir la devolución del dinero.

por Gosforth

•10 de Jul de 2019

My feeling is that the author of this course has no idea what is "Machine learning" - I have the impression that he repeats slogans which he does not understand.

por Romie C M

•8 de Jun de 2020

A good set of questions contain only one best answer and that is in measurement and evaluation.

por Ruslan Z

•23 de Out de 2020

theory is intuitive and ok but rated program assignments are just waste of time.

por Rishi A

•4 de Dez de 2019

Locked assignments are really frustrating.Why to wait till a specific date?

por Siddharth K

•1 de Abr de 2020

Python should have been great language for this course.

por Malcolm N

•11 de Jan de 2016

My CS friend recommended me to take this course to learn more about how to use data in business, after he heard that I wanted to program an app for food. he warned me about the great deal of math involved (mainly linear algebra). me being a physics/engineering major I naturally got even more excited (it turned out that he was right, and it would also be a huge plus to know multivariate calculus, and I can see myself struggle with the concepts had I not studied both these topics to bits in school). incidentally, this was my first online coursera experience. I can tell you it will be life changing experience. No longer do I have to physically travel somewhere to listen to lectures or hand in assignments, nor download lecture notes off of the school server. This is a 24/7 always on always available service, with the best TA's to answer your questions if you get stuck on homework assignments and quizzes. Everything in the coding assignments tests your knowledge of the course lectures and is designed such that you can complete it in the shortest possible amount of time while reaping the maximum amount of benefit. It is "easy" sense does not require you to grind through mundane things like looking for your own training set data or writing code to plot and visualise the data, but it is "hard" in the sense that very often it takes an hour (or more) of studying the lectures and thinking to figure out how to solve the problem in the most efficient way as possible which often involves writing a single line of vectored matlab/octave code. It is more of an overview of the most important topics in machine learning, but will be a great springboard to go in depth into each aspect of it. Lastly, Andrew often offers wonderful insights into the day to day of machine learning professionals in his lecture videos, so I would advise watching every single minute of them to get the most out of the course instead of aiming to race over the finish line (which can be tempting at times when the deadline approaches)

por Daniel D

•10 de Jul de 2018

This course is vital. People can do machine learning using out-of-the-box tools like keras, fast.ai, theano, tensorflow, and do amazing things. But to understand what's going on internally, to understand what it takes to get things to converge fast and to perform accurately and to be as useful as possible, to understand various types of networks and new discoveries later on, it really takes a good, healthy, rigorous foundation at least in very simple calculus, matrix algebra, back-prop, stochastic gradient descent, linear and logical regression, and such. If you try to forge ahead and get stuck or cannot come up with a way to build a proper model later on, you may find yourself giving up or returning to the material provided in this course. Andrew Ng did an excellent job teaching this. Even so, I heartily recommend watching views from others to get unstuck or to reinforce what you have learned--to make it more concrete. And do all the assignments aiming for 100% on every one.I found myself viewing youtube videos from many experts and found most of them extremely interesting and exciting. By getting several people's perspective, I feel I was able to learn the material better and more easily. Of course, it helps to have a math background, too, and I received my BA in math long ago from Fresno State with an Applied Math option and a Physics minor. It was a joy to return to my old math stomping grounds.If it takes time to get through, that's OK. Sometimes it helps to let the material marinate or let your brain marinate in the material. Then if you're like me, you might come to the place where you start to get on a roll and decide you need to put everything else aside and focus on finishing *this* course to perfection. And it can open the door not only to interesting work but to other interesting and worthwhile certifications.

por Tejas R

•26 de Mai de 2020

I found the Machine Learning course has a good structure, excellent teaching instruction and a perfect pace for working professionals. It covers a wide variety of topics/techniques in Supervised and Unsupervised Learning.

Professor NG has an excellent way of teaching any given topic. He covers all the fundamentals or building blocks to a particular topic quite well before putting it all together to demonstrate how a learning algorithm can be built. Each week has some quizzes and programming assignments you need to complete. For someone who is new to this entire topic, I found the quizzes and programming assignments sufficiently challenging. The quizzes test the basics covered in each topic, whereas the programming assignments give a hands on experience in how to write parts of Machine Learning algorithms.

I was also impressed with the course resources. There are numerous resource links available if you are interested in reading more into any topic. And the course forums are quite helpful in case you are stuck on any particular problem. Just going through the forums’ FAQ is bound to help you gain further insights into the course topics.

I am a working professional from whom it is difficult to dedicate sufficient time to enroll in a proper university course. I found the pace of this course well suited for the amount of time I was able to spend in a week.

This course does not cover any one particular topic in too much depth. It is structured to introduce you to a wide range of topics in Machine Learning and can set you up with the proper introduction and background if you wish to pursue any of those topic into further depth.

Overall, this course was very fulfilling and I would highly recommend it to someone who is looking for a course which introduces you to a wide variety of topics in this domain.

por Pat L

•30 de Nov de 2019

This course is an essential tool. I am beginning on a long journey of machine learning I hope will end at my ultimate goal of securing employment in the field of natural language processing and deep reinforcement learning. Starting completely from scratch, I began this journey by getting text books on the topics and attempting to follow along. Many of the basic learning algorithms, which seemed so daunting at first, were explained to me in a way that allowed me to fully embrace and understand the topics on an intuitive level. This course is the essential entry point to anyone wanting to truly understand the mechanics of machine learning. The mathematical concepts are broken down in a way that is truly intuitive and easy to follow. Additionally, Andrew Ng is a world class instructor. His manner, presentation, and encouragement from, at the time of this review, 8 years ago is evergreen and invaluable. He sincerely believes anyone who puts the time into learning this material can accomplish great things in the world. This course was inspiring. I was so engrossed with the material that I completed all 11 weeks of course work within 5 weeks. My only issue is the use of MATLAB/Octave in this course. All the materials I have read state that these languages and applications are widely not used in the field anymore though at the time of the course development, I understand the inclusion. Perhaps an update to the course that allows for the programming to be done in Python or R would be beneficial, but once you get the hang of MATLAB the programming exercises become easier as the course moves on. My sincerest and most heartfelt recommendation goes to this course for anyone who has an interest in opening the door to their own journey into this field.

por Alistair W

•6 de Jan de 2019

I worked for a start-up specialising in AI and rules based software that aims to learn how attorneys review contracts to extract key data points (e.g. clauses, parties, names, dates, numbers and text classification). Although I worked in sales / presales and as a domain expert for legal (being an attorney), I always wanted to know more about the technical side of what we were trying to achieve. This course provided my first proper and thorough introduction to machine learning, not only the coding concepts but also the underlying maths. It's been really tough going through the course, but that's down to my rusty maths (I have a maths A-level (i.e. pre-University maths to American readers)) and hadn't practised maths in around 12 years before completing this course. Similarly my coding skills had become rather rusty. All that said, the quality of teaching, forum support and coverage of this course has been great. I've had to read around quite a bit, mostly where certain topics were introduced without first zooming out to explain what the overall algorithm is trying to achieve. However, these issues were easily overcome given the course is well supported on the forums and similar topics are covered elsewhere on the internet given their prevalence today. I thoroughly recommend this course to anyone interested in AI and machine learning. I am looking forward to completing the Deep Learning specialization course as a result of this course, and will also be completing FastAi in tandem to get both a bottom up understanding (as was the case with this course, and will be the case with the deep learning specialization) and a top-down understanding (as will be the case with FastAI). Thanks to the team at Coursera, the forums and Andrew Ng!

por Zdenek V

•26 de Ago de 2015

My review concerns partially the course and partially the Coursera concept as this was my first on-line course and I cannot distinguish between those two.My experience was simply great, I felt that it was time well spend, for example compares to my company provided trainings and I'm hoping to return to another course.I have quite strong background in statistics so some parts were too basic for me but being able to speed the videos up is neat and it opens the course to wide spectrum of students (again opposed to company provided trainings or even school lessons where in the best case half of the students are bored of the slow tempo and other half isn't able to keep up with the same tempo). To have my own timeplan also helps but I even finished quite early as it was so fun.About the course contend: Andrew Ng prepared great lessons, all was pretty well explained (even when he cannot use some "advanced math" not to lose part of the students). The examples were illustrative as well as realistic (at least seemed realistic and that makes it more exciting than some artificial ones).I was little afraid of the programming exercises as I've never worked with Matlab (using mainly R). But now I'm glad that I know another language:) The system of submitting works perfectly.I can say similar thinks about the contend of the exercises as I wrote about examples in lectures. It is nice to program my own spam filter - the concept of programming only some small parts of the program and not to have to deal with data loading, plotting... is again very entertaining and instructive and then it can be done some nice application in short time.My thanks to the whole Coursera team and of course Andrew Ng. Hope to use this cool tools in my work.

Zdeněk

por Natalia G

•20 de Out de 2020

First and foremost, I would like to remark on the following. Some of the reviewers below note that this course is a sort of "waste of time because it covers only basics without going deeper into math". Please do not take it too personally, but there is a "Beginner" level which is clearly stated in the course. I am not going to argue with those who already have some basis in Machine Learning or Math/Calculus/Linear Algebra - for these people it may be useless, no offense. In this case, YES, this course is too introductory but, again, it is for BEGINNERS! It is like, I am a person with C2 English certificate but will complain about the English course for A2-level-students. Please, keep this in mind.

As a Beginner myself, I would like to express my great gratitude to Professor Andrew Ng and his colleagues who helped me to go through the Machine Learning and realize what I really want to do in my life! The course really covers basic approaches (methodologies, basic theory, some not very difficult practice which can be challenging too, and if you have little time, do not take this course, you need to spend a fair amount of time here to gain understanding). Since this is only a basic course, some information may be insufficient, but Professor Andrew Ng provided necessary books and materials in Resources section if anyone is interested in further development.

Moreover, I would like to emphasize the structure of this course. No "offtops", no "overmath", no any extra unrelated information - only what you really need to be INTRODUCED into this topic. I strongly recommend anyone, who is interested in Machine Learning, taking this course asap! You will never regret it, and as some of the reviewers mentioned, worths any penny claimed!

por Felix E

•2 de Out de 2017

Absolutely fantastic course! I just wish my old university had classes on that level.

Fantastic tutor as well. I loved how Andrew would sometimes explain the same thing in different ways and spend time to start explaining the problem before offering a solution. I feel like this has been a great approach, especially since you can't directly ask questions in an online course and it would have been really hard to figure out the details on your own if some things are not clear to you.

Best part of the videos are the times that Andrew messed up something and probably thought "ah I'll cut this part out later" but never did. So sometimes you'll hear him stop in the middle of the sentence and just start all over. Fucking hilarious.

My only criticism (or rather, room for improvement): The programming exercises were kind of bland at some point since they didn't really require you to think about what you are doing. Most of the time it felt like I would just try to figure out the Octave language while basically transcribing the formulas in the PDF's into Octave syntax. Maybe offering another language (eg. Python+Numpy) as alternative would help here: Since developers will be more familiar/comfortable with a language such as Python, they can focus more on the actual ML implementation instead of spending about 75% of the time trying to figure out the syntax. Seriously, fuck the Octave syntax. And the IDE is atrocious.

But overall, fantastic course, 10/10, would recommend to everyone interested in ML. This course has basically single-handedly gotten me a job as ML developer since the first four weeks were enough for me to absolutely nail the technical job interview. Thanks Andrew, if I'll ever meet you, I'll buy you a beer!

por Ron M

•5 de Nov de 2017

This course is a great balance of practical implementation and theoretical underpinnings. Very thoughtfully taught. My only complaint is more of an issue with the Coursera platform. If you run into problems, that in a physical university setting you could review in person with a TA, you can get help via the forums, but for the programming assignments in particular it is a challenge to talk to your peers about your problem, not violate the honor code, and still get to a point where you get your issue resolved so you understand how to complete the assignment. For example, while vectorized solutions are not essential for this class, they are highly encouraged, and in at least one assignment non-vectorized implementations pay a significant performance penalty (hours of run time - possibly to find it did not work properly). Ideally, in a physical college you could review things in person with a TA in a manner that did not violate the honor code, but did also get you to a true level of understanding. The open, public forums are not a substitute for that level of help, and while the "mentors" are good, helpful folks, they are also volunteers with their own lives and also limited by the Coursera platform itself. So that is the ultimate weakness with the Coursera format I am not sure the best solution, and it did not overly penalize me, but I can see people (especially on the Neural Network programming assignment) giving up and not completing the course because they could not get the level of understanding that is needed. I would still recommend Coursera, but hope as implementations are iterated the issue is addressed so that more people can get the help they might need.

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