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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....

SS

May 17, 2019

This is course just awesome. You get everything you wanted from this course. It covers on all topics in detail, helps in getting confidence in learning all the techiques and ideas in machine learning.

VB

Oct 03, 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

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автор: Fernando O A

•Dec 11, 2018

Excellent course! Andrew knows how to teach a subject that is not very trivial.

He uses a language that requires an abstraction of mathematical concepts, but without requiring a deep knowledge of formulas and calculations. He also manages to demand the least possible advanced knowledge, but it takes a little more dedication in programming and reasoning, achieving the goal of being understood through the preparation of well-elaborated exercises.

The Octave programming language is very simple and allows the dedicated effort in the exercises to really focus on understanding the algorithms and not on learning a new language.

Congratulations to Andrew, Coursera and others involved in the preparation of this course. I recommend everyone who wants to understand the basic concepts and algorithms related to Machine Learning.

автор: Oei L

•Mar 09, 2017

Excellent! I'm amazed at how much I learned. If there's is an Academy award for online courses, this would win the "best picture" award (and I wish there is to help highlight well-developed courses). Lectures, tutorials, resources were great, clear and most importantly, effective in communicating concepts, algorithms and developing the necessary programming skills (and I'm saying this after trying many others and giving up mid way). I have no doubt that this course will continue to stay relevant and evolve as this field as well as online delivery format progresses. I'm very very thankful for having access to such wonderful training material, made possible by founders of coursera, which I understand to include Prof Andrew Ng. I'm also very inspired by how coursera democratizes access to courses from top universities.

автор: Siyabonga H

•Jul 29, 2017

I am deeply grateful to Professor Ng and all collaborators and contributors who have invested their resources into making this course a huge success. It has been very meaningful to me as a lover of learning new things and acquiring knowledge. I took the course to get my foot in the door of ML/AI. I one day hope to be an academic researcher in either this or another related field. I cannot thank the community and course faculty enough. The concepts presented are explained very clearly, pitched at an appropriate level, and the course is always engaging and thought-provoking. Thanks again.

As others have pointed out, it may be worthwhile to perhaps consider offering Python versions of the programming exercises alongside the Octave/MATLAB ones, given the ubiquity and industry preference for Python on ML implementations.

автор: Philip R K

•Apr 03, 2019

The practice with applications in the programming assignments in this course was by far the most helpful aspect. If anything, I would have liked to have more programming assignments, and for some of them to be more challenging in terms of having the student build tools from the ground up (though I know most are available in Octave and Mathematica libraries). I also would have liked more mathematical derivations/proofs at times, but I recognize that those were arguably outside the scope of the course. Andrew did a great job explaining concepts clearly and the forum moderators apparently put in exhaustive effort to answer students questions and creative a very useful reference for other users. Thanks for putting in the time to create this great resource, especially writing out all the programming assignment content!

автор: Gerald A S M

•Apr 22, 2018

Creo es el mejor curso con el que se puede comenzar en esta ciencia, 100% recomendado, no solo aprendes los conceptos, también los aplicas en octave/matlab, lo cual te da una idea de como comenzar a desarrollar en otros lenguajes, cuesta un poco adaptarse al ritmo del curso comparado con otros cursos en linea, pero vale la pena el esfuerzo, a las personas interesadas en el curso les recomiendo adelanten lo que puedan para que lleven bien el tiempo especialmente en la semana 9, la cual considero para mi fue la semana que mas tiempo me consumió, sin embargo el tema es bastante interesante para los que nos gusta tener una pequeña visión de como pueden funcionar algunas de las tecnologías de los gigantes en la red, de igual forma les recomiendo lleven sus propias anotaciones en una cuaderno de repaso siempre es útil.

автор: Juan F C C

•Dec 11, 2017

Where to start? This course is one of the best I have ever taken in my life:

Andrew explains every subject clearly, I was surprised of his talent when teaching, a really admiring and uncommon thing. How come that most of my professors were not nearly close?!

He tackles every aspect of a subject you may wonder and also every aspect you may not but that is critical in practice. Indeed, the course gives a good deal of insight into the practical matters. The programming exercises are highly recommended, don't be shy.

I took this course for personal interest and got to feel so motivated that I felt joy when progressing, when rushing to meet deadlines and when completing it (I am sincerely sad it is over now). I am extremely satisfied with the experience this course was, I'm going to miss these past three months.

автор: Ryan D

•May 03, 2017

A very helpful and informative class! I don't know why it's listed as an advanced level course. Even with my limited Machine Learning knowledge and absence of experience with Matlab/Octave, everything was easy to grasp. Concepts were presented well. The summarized readings of the videos and lecture slides worked well as references. I would have hated having to go back and watch multiple times when I forgot something.

One thing I would have liked to see more of are the programming assignments. The automated submission system worked well and was helpful for making sure I had each step of the assignments coded correctly. I started doing the assignments before I took the quizzes, due to how well they helped me learn the material.

Would definitely recommend to anyone interested in learning more about Machine Learning!

автор: John R

•Sep 01, 2016

A very good introduction to the subject that moves at a good pace but doesn't make such big jumps that it is difficult to follow. Andrew Ng presents the subject clearly and gives some good insights.

One of the most valuable aspects of the course are the assignments where actual code is developed and worked upon. The code provided gives a good starting point for people to move forward and develop their own applications as they progress further.

I would like to thank Professor Ng very much for producing this course and making it available so easily. Also to the people working with him to mentor those participating on the course.

The course has very much wet my appetite for machine learning and I am now keen to pursue the subject further both through developing my own applications and learning more about the subject

автор: Rahul R

•Sep 27, 2016

I enjoyed this course, its balanced, easy enough for a general software programmer to start off into this math world. One thing I would wish for is a lesson exclusively talking about all the available options for any production needs, and talking about the different jargon that is used out there in the ML world and how we can relate it back to the jargon used in this course.

For example, when I try to use R, I get to know that I need not code to start with and I can use off-the-shelf packages like 'caret' to quickly try different models and compare their performance. But the real trouble I felt is when I tried to analyse their results, as I see too many new words and representations that are used, like Kappa, Specificity, etc.,. I wished these kind of very generic things were also quickly covered in this course.

автор: Srikanth R N

•Feb 11, 2018

First of all, I am deeply indebted to and congratulate Prof. Andrew Ng and Coursera for providing such a valuable course for free of cost. The option of buying the course is also highly appreciated.

I felt that the exercises are designed in a manner that though mathematical operations/calculations required are few, still, they felt tough because they need understanding the problem which is similar to real life problem where a lot of time will be spent on properly understanding the scope and context of the problem apart from mathematical calculations. Thus they help the student prepare for real-life problems. The Quizzes are also of good quality.

The support of Mentors especially Tom Mosher is exceptional and community feedback is great.

Thanks for providing me this opportunity to learn Machine Learning.

автор: Florian R

•Dec 28, 2017

I highly recommend this course as a starting point for every student found of data science, artificial intelligence and machine learning. Professor Ng is one of the best instructor you could find in a MOOC. He knows how to walk us through complex ideas in a simple manner and, through his own passion, arouses our interest for this enjoyable subject.

You don't need a mathematic background nor a solid programming experience to take this course; just motivation and commitment !

The team of mentors acting behind the scene are also very talented people who are very reactive to answer questions .

Thank you for making this machine learning course for beginners and let's enjoy this introduction to the vast field on AI, which is, according to Max Tegmark in his book 'Life 3.0', the most important conversation of our time.

автор: Rahul K

•Aug 08, 2017

I learned a lot of concepts in an organized and structured manner in this course that I probably would have spent months trying to figure out on my own. Prof. Ng is one of the best teachers I have had, in spite of the fact that this was an online course. Thank you so much for providing these resources for the world to learn from. This is nothing short of a miracle. Suggestions for the course would be - updating lecture notes for the latter half of the course and maybe more mathematical rigor. If it is not possible to add more rigorous math to this course, links to material that cover the topics in more depth would be nice. Also, can we have a Machine Learning 2 course that would cover some of these topics in more detail plus some new topics? I'd take that course in a heart beat, as I am sure many others would.

автор: Haris M

•Jul 20, 2017

This was my first class in machine learning and in this class I have learnt a lot. Basically before this class I was wondering that there must be some magic behind the fact that machines can learn to do things and then can improve themselves too and after taking this course I now know that all that magic was just mathematics and some basic steps. This course is great and I highly recommend it to those who know nothing about machine learning. This should be your first course in my opinion. Also, do the assignments with full concentration and try to read the code of each and every function to get an in depth understanding of the algorithm. Andrew is really an amazing instructor. This course will make you capable of applying the machine learning concepts to build some cool systems of your own. Highly recommended!

автор: Ryan J

•Jul 13, 2016

Loved this course! It was nice to get a real world comparison of the runtime of perfect mathematical solutions to problems like optimization vs. algorithmic solutions. In my experience, math classes tend to favor solving for variables algebraically, when the calculation of such a solution in many cases apparently takes much longer than the very accurate approximation obtained by gradient descent, for example! Also, I must admit I got a rush from implementing my first neural network, even though it was a lot simpler than I assumed it would be. I thought it was so cool to have built an autonomous program that can read numbers from a pixel array! Finally, I've always found the topic of data compression to be fascinating, so having algorithms and analysis tools for compression is pretty helpful. Thanks, Dr. Ng! :D

автор: Vinay P d L R

•Sep 05, 2017

Very well explained, and the subject is extremely interesting and contemporary. Made for people with almost no experience. But, it's also great for people who already have more in depth statistical/computational knowledge, as you can simply skip videos whose contents you already know. The programming assignments are also really good at testing your knowledge, and they are very satisfying to complete. They don't require many lines of code, but you really have to understand what you're supposed to do when you write the few lines code that are actually required. I'd like it to be longer and perhaps go a little deeper, but I guess that can be done through other courses (Like Dr. Ng's recently released Deep Learning Course), so this course is complete, as far as it's main purpose is concerned. Pretty much perfect.

автор: Samir S S

•Oct 11, 2016

I really like this course, specially the way Prof. Andrew Ng explains mathematical intuitions behind algorithms and concepts. I have done many courses in maths including linear algebra, statistical analysis ,signal processing etc, but never seen any professor explaining mathematical aspect behind algorithms, concepts in simpler way. I really like to thank Prof. Andrew for teaching this course so that people like me understands some of the concepts which were confusing to understand in past. After finishing this course, I feel confident in not only in applying algorithm in application but also to improve algorithm performance. Once again, I really like to thank people who made this course possible.

Thanks for you very much Prof Andrew for devoting your precious time to teach this course to students like us.

автор: Stéphane S

•May 10, 2019

Machine Learning by prof. Andrew Ng is excellent! In my opinion, it is among the very best courses on Coursera. It is thorough, well taught, and provides extensive learning materials, and lecture notes. Each lesson comes with graded programming assignments, and has detailed instructions on how to apply the knowledge easily.

This is an entry-level to intermediate course, which is very accessible as an introduction to machine learning, and provides a solid foundation in the underlying mathematics. As such, it does not cover hard ML issues. The knowledge covered is greatly useful, and can be readily applied to learn complex models for a wide range of applications.

Those looking for more advanced concepts should consider following up with deeplearning.ai's Deep Learning Specialization (also by prof. Andrew Ng).

автор: Rajeev Y

•Sep 05, 2020

I listened about this course from one of my friends during my graduation days and Since then I always wanted to complete it, I tried once earlier and couldn't complete the deadlines, but this time I completed well before the course end date, I am glad to overcome my mistakes. This course has formed my basis of ML and its concepts along with practical implementations has helped me in my job. Quizzes require good insight about the algorithms and thus encourages to follow course material carefully, following course material is enough to complete quizzes and practical assignments. I would like to thank course Instructor and all mentors for all resources and help provided through the course. Special thanks to Coursera for making this course as an open course to help all students learn ML and its applications.

автор: Alexandre B

•Mar 19, 2020

It was an honor to complete this course by Andrew Ng. The scope of the course is well defined and the professor respects it by not spending more time than needed on some maths concepts such as calculus. Being someone who is familiar with calculus I was pleased to see that the course focuses on machine learning and does not waste valuable time teaching calculus. That is not to say that there is no maths in the course because there is but the level is fairly accessible even if you don't know what calculus is. I would have appreciated a little bit more hands-on exercices between assignments but that is a subjective opinion. The course has a lot of theory but every week you get an assignment to validate the theory. I would recommend this course to anybody who wishes to get started with machine learning.

автор: Christopher P

•Jul 22, 2018

Excellent overview of Machine Learning. The goal of this course is to explain the main ML algorithms from a practical, intuitive perspective. This is accomplished using real-world examples, and the key ideas and techniques are reinforced by Matlab/Octave exercises. [No prior Matlab/Octave experience is assumed, and in fact, this course allows one to pick up the basics easily, if needed.] The key mathematical results that underlie the algorithms are presented, but there are no rigorous derivations or proofs. Some differential calculus and linear algebra background would be helpful, but it really isn't necessary to do well in the course, and to apply the various algorithms successfully. Finally, I found the notation consistent and clear throughout the course, and this helped to tie things together.

автор: Karan R

•Jun 21, 2016

The best course I've taken on Coursera so far. This was the first ever course offered on Coursera, by the founder himself, Andrew Ng. He's a great instructor, covering topics right from the ground to the sky. I'd say the implementation is in OCTAVE instead of popular languages like R/Python, which could have improved upon a lot for learners. But since Andrew has focussed this course upon beginners, I think OCTAVE is apt.

The assignments were relatively easy as most of the implementation (ground work) was done, only the main functions were to be implemented. But yes I learnt a lot from the way the assignments are designed. You create a digit recogniser just by being through this course.

I would recommend it to all learners who are beginning with Machine Learning or Data Sciences to take up this course.

автор: Denis O

•Feb 16, 2019

Great introduction to Machine Learning.

It gave me exactly what I was hoping for: at the end of the course, I feel like I can look at a typical machine-learning / AI / neural network program and understand how it might work (of course, a specific program mightn#t work that way, but I#d know one way that it could work, and the type of results, predictions and flaws to expect.

The programming exercises were very helpful because they forces us to think and to refresh our knowledge of linear algebra. I would probably have made them a little bit harder - not that they were easy for me at all !!! - in the sense of ensuring that we had to always program the critical code for the key topic of a given lesson. But maybe that#s not realistic.

The lecturer is phenomenal - very clear, very precise, very engaging.

автор: Abdullah S

•Oct 25, 2017

Just Excellent, everything about this course is just fantastic, beginning with Prof.Andrew, passing with his passion for the subject and his motivation to really make you understand everyword he says, he is keen on delivering all this expertise and this alone is a fine quality, the course is well organized and the quizzes and programing assignements are to the point and are a very good exercise, I just felt the course needed 2 small videos one addresssing the differences between linear regression, logistic regression, SVMs and Neural Networks and another video exciting people by a small example of machine learning on self-driving cars (very small programming assignments to help excite people and give them an-overall idea)

again Thanks to Prof.Andrew and all who helped me find my hobby and passion :D

автор: Xiang L

•Jul 10, 2017

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. It was so great and very helpful of implementation.

автор: Lokesh N

•Jun 15, 2020

Immensely helped me to start off with Machine Learning. Every concept is deliberated beautifully by the instructor. He has great expertise in the field and understands especially on how to teach or guide the novices and think from their perspective. In this regard he delivers what is concretely required. This course bridges the gap between applicability of the algorithms and mathematics behind it without deeply diving into the latter. If someone isn't well versed with Linear Algebra,Calculus and Statistics they might find themselves impatient for superficial explanation in this regard. This course would have been ideal if it's worked out with the current language in demand and bit more practical. Overall, In my opinion I would definitely recommend this course for all the beginners.

Heartfelt thanks!

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