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Machine Learning

$71.00

(5 customer reviews)
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Prices differ based on Coursera’s programmes.

Coursera might get your enrollment for free, you only pay for the certificate.

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, data mining, 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.

5 reviews for Machine Learning

  1. robert g c j

    Overall the course is great and the instructor is awesome. Machine learning is fascinating and I now feel like I have a good foundation. A few minor comments: some of the projects had too much helper code where the student only needed to fill in a portion of the algorithm. I would have preferred to have worked through more of the code. Also, there were a few times when the slides didn’t contain the complete equations so it was difficult to piece it all together when writing the code. Lastly, I wish that there was more coverage on vectorized solutions for the algorithms.

  2. vasily

    I’ve never expected much from an online course, but this one is just Great!

    Even if you feel like you have gaps in your calculus/linear algebra training don’t be afraid to take it, because you’ll be able to fill most of those right from the course material or at least figure out where to look.

    This course gives grand picture on how ML stuff works without focusing much on the specific components like programming language/libraries/environment which most of ML courses/articles suffer from.

    This leaves you with freedom to pick it yourself and apply gained knowledge however you want.

    Biggest takeaway for me as a person working on my own project is amount of attention professor Ng brings to methods of evaluating your ML methods efficiency and how this correlates with time/effort you should put into the specific system component.

    Because i feel like this is where most people slip up in practice.

    Great thanks for all of that!

  3. anhhuy

    I am Vietnamese who weak in English. To learn this course I have to choose playback rate 0.75.

    But the teacher – Professor Andrew Ng talks clearly and the way he transfer knowledge is very simple, easy to understand. Myself is excited on every class and I think I am so lucky when I know coursera.

    This course provide a lot of basic knowledge for anyone who don’t know machine learning still learn.

    Once again, I would like to say thank to Professor Andrew Ng and all Mentor.

    (I hope all of you understand my feeling because of my low level English, I cannot express it exactly)

  4. rajeev a

    This course has of course (pun intended) built a formidable reputation for itself since it was laucnhed. I took the course in 2019 when it had been around for a few years and so what I am saying here may resonate with a lot of people who have taken the course before me. “Concretely”(!), Prof Ng takes the student on a very well structured journey that covers the vast canvas of ML, explaining not just the theoretical aspects but also laying equal empahsis on the pratical aspets like debugging or choosing the right approach to solving a ML problem or deciding what to do first / next. At that level this course is highly recomended by me as the first course in ML that anyone should take. I do have a suggestion to make regarding how some of the portions could have been explained more lucidly. These are portions that pertain entirely to the mathematics and programming problems, where I struggled for days and (for back propogation) for months before realising that maybe the explanation given in the slide wasn’t clear enough and at times i just needed to try really random ideas to get out of the programmin rut that I was stuck in. An advise for anyone doing the course would be to write down the matrices in full detail and do the transformations of cost fucntion and gradient descent or back prop using pen and paper and attempt to write the code for it only after once one is clear about the exact mathematical operation happening. Thank you, Prof Ng for gifting this course to the online learners community and I would also like to thank the mentors who have replied to the queries patiently while stadfastly enforcing the honour code.

  5. rohit s

    Andrew Ng is a great teacher.

    He inspired me to begin this new chapter in my life. I couldn’t have done it without you

    and also He made me a better and more thoughtful person.

    Thank You! Sir.

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