Voltar para Aprendizagem Automática

estrelas

162,032 classificações

•

41,566 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....

NN

14 de Out de 2016

It's a good introduction - not too complicated and covers a wide range of topics. The programming exercises are well put together and significantly help understanding. The free Matlab license is nice.

PT

31 de Ago de 2018

Sub title should be corrected. Since I'm not that good in English but I know when there're mis-traslated or wrong sub title. If you fix this problems , I thin it helps many students a lot. Thanks!!!!!

Filtrar por:

por Vyacheslav G

•23 de Fev de 2019

Sadly it's just introduction. And i would recommend to make course for python instead of matlab/octave

por Malcomb M

•21 de Jul de 2017

Content was OK, but quality of teaching was fair at best -- important points glossed over, many not made clear at all, some simply omitted: Bayes classifiers, decision trees, etc, etc.. Audio visual quality of lectures poor. Ng's onscreen scrawls and voice recording were terrible, and there were many mistakes in graphics. Numerous typographical errors in exercise instruction .pdf's. Exercise text itself (ex__.m files) had numerous "pauses" that failed to instruct the user what he had to do (or not do) next, so you had to carefully examine what followed. If more care was put into exercise construction, the "pause" text in the command window would not just say "Enter to continue" but say what coding action was needed to continue. Obviously a lot of work has already been done on interactivity: Quizzes, online Submit scripts, which for me all worked extremely well. But clearly the course could use a lot of improvement in many aspects. Thus I grade it: C-

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 Andrea A

•27 de Out de 2019

you have to teach this course with Python otherwise Octave is purely a waste of time. You need to keep up with time. Nobody in the financial industry uses Octave. Also, you need to show way more examples and exercises to allow students to absorb theoretical concepts.

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 pat

•15 de Fev de 2021

I'm glad I didn't pay for this one. The answers in the quizzes are not correct. I checked them. Also, they don't tell you until week 2 that you will not be able to use any strings in your files in Matlab and Octave, everything has to be a number. I'm not sure this is useful to anyone. Bec the answers are wrong in the tests you won't be able to pass any of the quizzes, I got 60% on them. I retook each of them 6 times. I even checked the answers on Octave. Whoever wrote the quizzes did a poor job. I also did not understand any of the homework labs. I tried doing them and there were no instructions and the scripts did not work. Unfortunately, I can't recommend this class. It looks like the person who did the videos spent a long time on them, but whoever wrote the quizzes and homework did not check anything. Really sad. They could have made some money off of this one. Just sloppy.

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 Lorenzo V

•23 de Mai de 2019

No math, purely intuition and drive through formulas not demonstrated. You can't improve after this course because you don't really know why you did what you did

por Abdullah D K

•18 de Fev de 2021

This is not a course, more like listening to the people who talks about machine learning and then writing your feelings about them.

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 Uri Z

•9 de Set de 2016

Very basic and superficial course. Apologies each time derivatives need to be used.

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 Aly E

•10 de Jun de 2021

I have to say Andrew did a pretty wonderful job in this course. I was a person with a very nice software development experience but never had to deal with machine learning. The last time I had to deal with calculus, algebra or mathematics in general was about 7 years ago (in Arabic, and having to deal with that in English is another story), thus I had approximately zero mathematics knowledge. Before this course, I attempted different approaches into this field but throughout them, I would either fall in a valley of philosophy or I would have to stop every few minutes and check the mathematics behind what's just happened.

The way Andrew approached the content in this course makes perfect sense to me (and I assume, to anyone with similar background). He's not the kind of teacher who'd plot complicated things onto the board and tells you that you should use it, instead, he would build the components of everything bit by bit until it makes perfect sense. He also has a good estimate of how hard/complicated something might be/seem to new comers and thus he instructs you throughout the course to be gentle on yourself if you don't get it at first.

Also, the vast majority of quizzes and programming assignments in this course put you in situations where you have to deal with tricky confusions in order to work things out and thus try to make sure that you have a deep understanding of what's going on.

I also like the quality of the content provided in this course. Andrew didn't just tell you "hey, here're the algorithms and that's how you use them, go use them", instead, he dedicated a decent amount of effort trying to explain how to choose which algorithm and when and why, and how to "not depend on gut feeling" but instead diagnose and debug different situations you might find yourself in.

Judging by earlier approaches I attempted before this course, I believe that it might've taken me a very long time to obtain the knowledge provided in this course.

One minor draw-back of this course is that unlike the first half, the last few weeks don't have reading recap after each video session. Another one might be the fact that the weight of this course (in terms of time and effort needed to complete something) is not equally distributed across the weeks (one programming assignment took me almost two weeks to complete, and two weeks in the course took me one day to complete).

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.

- Analista de dados do Google
- Gestão de projetos no Google
- Design de UX no Google
- Suporte de TI do Google
- Ciência de dados da IBM
- Analista de dados da IBM
- Análise de dados da IBM com Excel e R
- Analista de Cibersegurança da IBM
- Marketing em mídias sociais do Facebook
- Desenvolvedor de nuvem full stack – IBM
- Representante de desenvolvimento de vendas da Salesforce
- Operações de vendas da Salesforce
- Suporte de tecnologias da informação do Google
- Certificado profissional de suporte em TI do Google
- Automação da TI do Google com Python
- DeepLearning.AI no TensorFlow
- Certificações populares de segurança cibernética
- Certificações populares de SQL
- Certificações populares de TI
- Ver todos os certificados

- cursos gratuitos
- Aprenda um idioma
- pythonpython
- Java
- web designweb design
- SQL
- Cursos grátis
- Microsoft Excel
- Gestão de projetos
- Segurança cibernéticaSegurança Cibernética
- Recursos humanos
- Cursos gratuitos de ciência de dados
- falar inglês
- Redação de conteúdo
- Desenvolvimento Web completoDesenvolvimento Web Completo
- Inteligência artificial
- Programação em C
- Habilidades de comunicação
- Blockchain
- Veja todos os cursos

- Competências para equipes de ciência de dados
- Tomada de Decisões Baseada em Dados
- Habilidades de engenharia de software
- Habilidades Pessoais para Equipes de Engenharia
- Habilidades Administrativas
- Habilidades de marketing
- Habilidades para Equipes de Vendas
- Habilidades de Gerente de Produto
- Habilidades Financeiras
- Projetos de desenvolvimento de Android
- Projetos em TensorFlow e Keras
- Python para todosPython para todos
- Aprendizagem profunda
- Habilidades em Excel para negócios
- Fundamentos de negóciosFundamentos dos Negócios
- Aprendizagem Automática
- Fundamentos da AWS
- Fundamentos de engenharia de dados
- Competências de análise de dados
- Habilidades para designers de UX

- Certificados MasterTrack®
- Certificados profissionais
- Certificados universitários
- Graduações em negócios e MBA
- Graduações em Ciência de Dados
- Graduações em Ciência da Computação
- Graduações em análise de dados
- Graduações em Saúde Pública
- Graduações em ciências sociais
- Graduações em gestão
- Graduações nas melhores universidades europeias
- Mestrados
- Bacharelados
- Graduações com uma trajetória de desempenho
- Cursos em Ciências (BSc)
- O que é uma licenciatura?
- Quanto tempo leva um mestrado?
- Um MBA on-line vale a pena?
- 7 maneiras de pagar pela pós-graduação
- Ver todos os graus