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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....

RD

30 mars 2018

Perhaps the greatest instructor and the greatest course, I enjoyed it so much I had continued to do it in between my exams and looking forward fto start or deeplearning,ai specialization in a few days

NN

14 oct. 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.

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par Arpit S

•3 août 2019

This course is brilliant. And yes just because its almost a decade old course doesn't mean the information is outdated or not useful. Infact, it is a complete opposite. This course is legend. At first I had the same feeling as should I start with this course... as many people recommended doing this before any other course. And it turns out that they were indeed 100% right.

The best thing about this course is that it teaches us the theory and many useful techniques in understanding the intuition behind many different machine learning algorithms. And yes this course uses Octave/Matlab as the language for programming assignments. Now many people will think that aren't they quite old and not used much anymore ( Octave ), but here's the thing... that this course teaches us such a good understanding behind these algorithms and the intuition behind them that the language we'll use won't really matter that much. And you can easily understand how versatile it is to implement those algorithms in any other languages. And also Octave is easy to learn. It's a prototype language ( I think? ) and so there shouldn't be any trouble understanding it, and if you know any other language already, then it will be walk in a park.

Finally, I would just like to say is that the video lectures in this course are really really really great. You will learn a lot from these videos, so you should definitely enroll this course if you're planning to do so. As the knowledge value in this course is absolutely epic!

par Kevin C

•31 juil. 2017

I highly recommend all of those who have data-related background, are extremely interested and fascinated about the topics of machine learning, and would like to start building their career in this field to attend this course as their first step. Professor Ng is indeed very knowledgeable and is also a great lecturer. Throughout this course he not only well introduced and led me through all the basic concepts and techniques of machine learning, but also illustrated all the important and practical tips for realizing a real-world project, which are aside from the techniques and can be easily ignored, but may save you a lot of time and efforts and guide you much more easily to a more proper direction of achieving your objective.

Some people may find the concepts and programming assignments within the course more at entry-level and very simplified to understand and complete, while I think the course is still extremely helpful to me, as 1) it builds a great structure with integration of all necessary techniques under the umbrella of the topics of machine learning, which makes it much easier for self-trainers to extend their study above and beyond the course 2) it provides a completed set of background and extensive materials (e.g. Professor Ng's Stanford course) for people like me to deep dive their study under each topic.

All in all, I really appreciate Professor Ng and Coursera to offer this fascinating course, and thankful to be involved in such a great learning experience!

par William Z

•8 déc. 2017

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.

par Sergey G

•28 juin 2016

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.

par Sotiria K

•19 oct. 2018

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.

par MachineLearner

•27 août 2020

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.

par Karl N

•20 déc. 2018

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!

par Tommi J

•16 juin 2020

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!

par Tim S

•22 août 2017

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!

par Jianan G

•21 nov. 2015

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

par Scott S

•27 juil. 2021

Over all a very good course with a lot of good content and information. I did think that it could be cleaned up a bit to remove some of the obvious errors and unclear points. For example some parts of the video that were obviously meant to be cut, and for some reason weren't. A few cases where there are uncorrected typos or errors in the homework assignments where you had to look in the discussion forums or resources to find out what was wrong (and why something wasn't working). There was also the neural network homework which was the hardest because some of the details on how to construct an actual solution to the problem weren't completely clear in the videos or the homework description - in particular when you should and shouldn't include the subscript 0 terms (in my opinion). I also think it wouldn't have hurt to maybe have a little discussion on the generalization of data and features from vector form to matrix form would in order to make doing the homeworks a little easier, as this seemed to be left to the student to figure out on their own. (Specifically, in a lot of the videos the professor talks about vectors of features and data to provide scalar answers, but the homeworks often use matrices of features and data to produce vector answers, and this can make understanding the material harder when you're still trying to understand and apply the main concepts in the lectures.)

par Robert D

•19 juil. 2019

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.

par Vinicius A R L

•30 oct. 2021

This is definitely the number one beginner's content online for the future Machine Learning practitioners. I am already an AI professional and actually started my learning journey with the free version of this course, a few years ago, but decided to take it again now just to get the certificate. After learning from many different sources including really strong formal education with top tier professors in my country, I still recommend this course as an entry point to anybody who asks me how to get into this career. If you are in doubt about the assignments, they are indeed really valuable. Specially the programming tasks. They are really well-elaborated so that you can focus on learning precisely the most important concepts and contribute a lot to the learning experience. All in all, a perfect balance between theory and practice with an amazing didactic approach. Clearly the result of a professor who has mastered the craft of teaching. I am not being paid nor getting anything whatsoever for this review haha I just happen to admire Andrew a lot not only for this course, but also the countless other extremely high quality contents out there. His contributions to the AI community is immeasurable and he will surely go down in history as a legend. Out of so many admirable and inspiring researchers and professionals moving us forward, to me he is the one true "hero of Deep Learning"!

par Nader A M

•3 août 2021

I am a lawyer with some very specific goals in mind that have to do with building robojudges and using legaltech to make the world a better place. People like Professor Andrew aid me significantly in terms of the knowledge they've instilled within me to give me clarity as to how I should go about such ambitious tasks, but they also do much more than that. With his hard work, dedication, and insight, Professor Andrew has inspired me to be more like him. I genuinely can't thank him enough for setting me down this path. As a side note, I also did not have any money to pay for the course; they country I'm from is in a complete state of collapse, and I could not afford a dime on online learning. Professor Andrew's generosity did not stop at curating impeccable videos and putting them out there free of charge, explaining very difficult concepts in clear and unambiguous ways using examples and practical illustrations that develop learners' intuitions as to how machine learning system works, but he, along with the Coursera team, made financial aid a readily accessible option - something that's helped me tremendously. Thank you so much. I have genuinely learned much more than I'd originally expected, and I'd implore any person who's hesitant about taking this course to not think twice: when it comes to Machine Learning, this is where your time would be most efficiently allocated.

par Richard H

•20 août 2019

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.

par Paradoks S

•13 nov. 2015

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par Jacob M

•6 janv. 2018

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.

par Melissa K

•9 févr. 2020

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.

par Pranesh

•28 déc. 2015

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.

par Vincent

•4 avr. 2019

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.

par Chin-Chieh, W

•13 avr. 2018

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.

這是一門我非常喜歡的課程，誠摯地推薦給每一位真心想學習機器學習的學員們。雖然機器學習絕對是當代最新穎、極重要的議題，但它結合了線性代數、微積分、程式設計及演算法等學問，機器學習其實是並不容易讓人親近，有好多的面向須顧及，所以自學時往往容易失焦，而不很能夠明白機器學習的作法及重點何在。而這也是我對吳恩達老師感到欽佩之處，吳恩達老師的教學，簡言之，深入淺出。即使學員沒有相關的背景，不曾學過微積分或是程式設計都無妨，幾個重要的機器學習的方法仍是能清楚明瞭，即使有些證明在課堂上並未提及。難能可貴地，幾個重要的機器學習方法，這門課程都有含括。我很享受這門課程，而且當課程將盡時，渴望著學習更多相關的事物。感謝您，吳恩達老師！感謝您給了我人生之中一門美好的課程。

par Alejandro O

•28 mars 2019

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!

par Juan E T B

•9 mai 2021

Es un curso totalmente recomendable.

Después de terminar el curso pensaba darle una puntuación de 4 estrellas ya que los videos son un poco anticuados y algunos conceptos eran difíciles de entender, además usaban programación con Matlab que no es el lenguaje de programación más usado actualmente para programar redes neuronales y algunas veces resulta complicado saber que esta haciendo con los datos.

Pero después de buscar información para continuar mi aprendizaje sobre redes neuronales me he dado cuenta que es un curso realmente completo y toca los fundamentos principales del funcionamiento de las redes neuronales, pero no solo mostrándote una serie de pasos a seguir, sino mostrándote las diferentes formulas y el motivo de su utilización.

Al final la calidad de los videos es algo secundario ya que se visualizan perfectamente, los conceptos son difíciles de entender porque son matemáticas avanzadas, pero si no las entiendes no entenderás como funciona una red neuronal internamente y solamente podrás ejecutar modelos hechos por otras personas, y Matlab aunque no es el programa mas utilizado actualmente tiene un uso de matrices sencillo que copian los lenguajes actuales, las operación son complejas pero también serán complejas con Python .

Recomiendo este curso a todo el mundo que quiera adentrase en el funcionamiento de redes neuronales.

par Ryan C

•11 avr. 2018

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].

par Matt D V

•6 mars 2020

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!

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