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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....

TP

25. Juni 2020

This course is a very applicable. Professor Ng explains precisely each algorithm and even tries to give an intuition for mathematical and statistic concepts behind each algorithm. Thank you very much.

BK

11. Juli 2021

I've learned a lot from this machine learning course. A huge thanks to prof. Andrew for guiding me throughout this course, and also Coursera for providing me with such a platform to learn this course.

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von Vincent C

•25. Sep. 2019

After finishing the course, I feel much more confident in pursuing more advanced machine learning. The course teaches everything intuitively and in detail but maybe it could use some improvement to achieve perfection. It would be better if the course could provide pointers to some of the topics beyond the scope of the course such as the derivation of the back propagation, svm, pca, etc. Because often times when you search for derivations they might not be very useful for your levels, if course could provide some good references as some lecture notes after the video would be great for the students to gain even more solid groundings of the things behind the hood

Super thanks and thumbs up

von Vamshi B

•6. Juni 2019

As a machine learning newbie, I can say this course is really helpful to get in depth intuition on how machine learning algorithms work. Techniques to evaluate and improve our algorithms are also explained very well. Programming exercises are really challenging. Review questions are also crafted well. Though this course uses Octave/Matlab instead of python for programming, I find it quite useful to understand and implement algorithms easily. Only negative of this course is, mathematics involved is not explained in detail. Overall, this course has helped me a lot to understand machine learning in a better and useful way.

von DEEPANJYOTI S

•11. März 2019

This is a very good course which gives a good solid foundation in the basics concepts of Machine Learning. Prof. Andrew explains reasonably complicated algorithms in a very intuitive way which goes reasonably deep, but at the same time doesn't overwhelm the student with a lot of underlying mathematics. The course structure also follows a very natural progression (linear regression --> logistic regression --> neural network --> SVM) and bringing in other basic concepts like feature normalization, regularization, measurements etc. along the way. Definitely one of the better designed courses I've seen so far.

von Tun C

•2. Feb. 2018

I've been working with machine learning for a while and I've used different supervised and unsupervised algorithms. However, this course taught me about how these different machine learning algorithms work under the hood. Professor Ng is a great teacher. His method of describing the problem set, giving the intuition on how to go about solving the problem and slowly defining the algorithm works very well. This course has the right amount of breadth by covering only the most applicable algorithms and has the right amount of depth by covering the math and the intuition behind each algorithm.

von Maria V

•6. Dez. 2020

This is the most amazing class that I have taken in a long time. The attention to detail is incredible. I appreciated the most all the context Andrew gives around evaluating algorithms and models, reasoning about finding errors and taking steps to improve the performance. This course gives you so much more than just the algorithms and makes sure you think for yourself and truly understand the topics.

One thing that I would suggest as an improvement is video editing, since sometimes sentences are repeated in a way that indicates that the previous sentence should have been edited out.

von Anith S

•6. Juni 2019

This is the first ever course I have taken on Machine Learning and I have to say that it was the best course that I have ever taken till I have taken the DeepLearinig Specialization by Andrew Ng.

I would highly recommend this course for anyone who wants to break into Machine Learning. Because it starts with the very basics and builds on it.

It currently may be bit outdated considering that it is thought using Matlab and not Python but it is excellent in explaining the core concepts and the algorithms of Machine Learning.

It is still a good course for breaking into Machine Learning.

von Zheng Y

•23. Feb. 2019

The course is very well structured for me, a student who has some understanding of machine learning but would like to get a systematic introduction of the subject.

The course strikes a balance between depth and breadth. The amount of math and equations are just right. Prof. Ng did a good job stimulating the students' curiosity to dive deeper. And for those who want to get practical and hands-on, this course contains enough tools for machine learning practitioners.

I would recommend this course to anyone who is interested in machine learning but do not know where to start.

von Zhou K J

•7. Jan. 2022

As a student previously engaged in many Machine Learning modules in school and ML-type projects, there are still many takeaways from this course in terms of its exposure to mathematical applications and clarifying some of the doubts and issues that have surfaced along my journey in the Machine Learning world. I would like to extend my thanks to Andrew and the Stanford team for creating such a wonderfully crisp and concise class on Coursera for everyone around the world to embark on their own ML journey. May the future learners have as much takeaways as I did!

von John W

•18. Aug. 2020

I would give this class 4.5 stars (rounds up to 5). Many different ML topics are covered, and they are presented at an appropriate pace for learning. The programming assignments are a great way to review the content and make sure you understand some of the details. Past experience with linear (matrix) algebra will be helpful but not required. Be sure to consult the resources that are available, especially the errata (it was a little disappointing how many small errors are present) and the lecture notes. But overall, I highly recommend this course.

von Walter E P

•23. Dez. 2019

Great Course!. I took this course after having been formally trained in topics such as Numerical Optimization, Neural Networks, Genetic Algorithms, Linear Regression and other topics and I found these classes to be both very informative and refreshing. Learned something that sometimes some courses out there forget to mention which is how to draw meaningful statistics to analyze your algorithms performance and also things like what do work on next. I definitely advice people to take this course even if you are a pretty advanced learner on these topics.

von Paweł M

•24. März 2021

Fantastic course! I highly recommend it to anyone who wants to look a little more "under the hood" of ML. There are many courses that simply teach you how to use certain tools, such as Pandas or Tensor Flow, but often without explaining what the algorithm does or what kind of math operations are involved. This course shows it, but fear not - it's not as mathematically advanced as it could be - just enough to understand the topic. Professor Ng is a great teacher, I wish my professors at the time I studied were like him. Thank You Professor Ng!

von Vivek R

•12. März 2019

This course is very well designed, covers a lot of topics with a lot of rigourous detail, but Andrew Ng introduces them giving some intuition about them, before diving into the deeper Maths. Assignments are very challenging, but with some boilerplate code already done, they are immensely satisfying, as you end up achieving with some implementations of pretty cool problems. I have done linear algebra and regression and PCA before, so was able to complete it rather quickly, but this should be very approachable and useful for everyone.

von Kohei K

•2. Okt. 2020

I am based in Tokyo, Japan and working for Marketing in Hewlett-Packard Enterprise. Marketing is now digital and data driven. In order to improve marketing data science skill, I took this course. This course and Professor Andrew Ng is amazing and could learn Machine Learning comprehensively. Recommendation system and clustering is very relevant to marketing job and would like contribute to the world based on the knowledge what I learned in this course. Many thanks for your guidance and great teaching, Professor Andrew san !

von Allyson D d L

•18. Aug. 2021

This course was so amazing for me, because I always wanted to understand more about AI and neural networks but I did not find these informations easily. But this course was a nice introduction about AI and machine learning and I am so excited to learn more and more about AI after this course. I also started my first personal project with the knowledge acquired with the course.

I have just a bad commentary because the last 2 weeks did not have programming exercises, and it was needed to train the skills mentioned in the videos.

von Tomasz C

•14. Apr. 2021

Bardzo polecam ten kurs, jak i wykładowcę. Andrew Ng świetnie przekazuje wiedzę, bardzo czytelnie i spójnie przedstawia cały materiał (w naukowy sposób). Na forum kursu można znaleźć wiele przydatnych informacji, a mentorzy pomagają i bardzo szybko odpisują na wiadomości. Świetne zadania z programowania (głownie w Octave) które opierają się na realnych przykładach i wymagają od nas zrozumienia algorytmów (wzorów). 100/100. Serdecznie dziękuje bo wiem że wymagało to dużo pracy, aby stworzyć tak dobry kurs.

von Harsh S

•9. Juni 2020

This course is an amazing and extensive resource for machine learning, that isn't afraid to dive into the math behind ML. I thoroughly enjoyed all the intuitive explanations and examples given by the instructor. By focusing on the core concepts of ML, rather than on a specific programming language or library, this course ensures that it stays relevant even years after it was released. Overall, this course may be a little challenging for some people, but it is certainly worth all the time invested in it.

von Jatin k

•18. Juni 2020

A very good course for beginners who want to study machine learning. Mr Andrew Ng is a very good teacher and very experienced in machine learning. The course structure is what it should be for an ML course. Programming exercises are really brainstorming and must be solved. Online threads can be used to seek help from other students and mentors, and are really effective. Reading slides are important for making notes.

This course is a very good and effective platform to learn machine learning skills.

von Subham

•3. März 2019

The real way to learn Machine Learning is this, no black box;understanding using pure mathematics makes it more interesting, and as I was solving the programming exercises I got to know, how simply vectors and calculus can be used to represent complex mathematical formulas. All the hours completing this course was worth. Once I started using machine learning libraries, all concepts were no longer black box for me, suddenly everything started making sense. Highly Recommended course for beginners.

von Mārtiņš R

•24. Apr. 2019

This was the hardest thing I've done in ages. I gave up at some point until a breakthrough in programming - I learning to use operations with matrices. I did all programming assignments in python. Couldn't finish the Neural Network - I was stuck for a month because I couldn't wrap my head around mathematical operation in backpropagation. Overall this was a journey. Every morning and evening learning on the way in the bus to and from work. Also lonely weekends. Finished. Can't thank you enough.

von Manish S

•4. Feb. 2020

It is an amazing course for beginners who wish to know about Machine Learning. Taking the course and getting high-level knowledge of how different ML Algorithm works can be very useful and (in some cases, it is must) before using any libraries to create solutions. And for such cases, this course is certainly one of the best.

I sincerely thanks to Andrew Ng for taking out his time to make this course for a student like us. I highly recommend anyone to take this course with no hesitation.

von Michael G

•23. Sep. 2021

A good course to be introduced to fundamental machine learning algorithms, including regression, neural networks, support vector machines, K-means clustering. You will learn the mathematical intuition behind these concepts. You will complete programming assignments to implement the various ML alogrithms yourself. You also come away with an idea of how some of the products of the biggest internet companies use ML. Overall the course is well worth your time.

von Emily C

•2. Jan. 2020

A great introduction to Machine Learning. Found the pace of the lectures just right with a good balance of theory, worked examples and practical tips. I did Maths with Statistics at university and so found some of the concepts familiar but great to refresh! The coding assignments were well-explained and was able to walk through them step-by-step with the instructions. Really enjoyed the course and excited to start testing it out on some problems of my own!

von Vaibhav J

•5. Juni 2019

The explanation of each and every topic is so simple and easy. The course is taught by prof. Andrew Ang and covers the major concepts of machine learning. He also provides a good intuition about the topic so to understand them better. Overall this course is awesome and I would highly recommend to someone who is a beginner in Machine Learning. I am very grateful to Professor, Mentors and the Coursera for this amazing journey of 11 weeks in machine learning.

von Issam B

•7. Dez. 2020

I'm 50 years old and never thought that a career change can happen at this stage. This course gives you the basics and knowledge of how your trained data is fitted into a model; then used to predict/estimate the output of your next set of data. More importantly, it gave me the confidence to go deeper into the field of machine learning. I'm enrolling to get certified in "Deep Learning Specialization" on Coursera. Maybe we'll meet in your next AI adventure.

von Anup P

•21. Mai 2020

This course i actually the first decent course I have taken in Machine Learning. It's really good if you have absolute no idea about what machine learning is. Don't fear the math, because Andrew (the instructor) really explains everything really good. Although a bit of programming experience is necessary, you can cover it while watching the lectures. I wanted to say the Instructor and the Mentors THANK YOU for sharing these extrordinary material with us.

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