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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....

CS

15 июля 2019 г.

The course will give you the incites to understand the data driven mathematical functions to write softwares that can behave or change its behavior, based on stimulus (data).\n\nAndrew Ng is excellent

SW

8 нояб. 2020 г.

Excellent course, highly mathematical overview of how introductory machine learning models work. Thanks to Andrew Ng for putting together a lot of great material and challenging quizzes and exercises.

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автор: Vamshi B

•6 июня 2019 г.

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.

автор: DEEPANJYOTI S

•11 мар. 2019 г.

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.

автор: Tun C

•2 февр. 2018 г.

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.

автор: Mariia V

•6 дек. 2020 г.

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.

автор: Anith S

•6 июня 2019 г.

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.

автор: Zheng Y

•23 февр. 2019 г.

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.

автор: John W

•18 авг. 2020 г.

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.

автор: Walter E P

•23 дек. 2019 г.

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.

автор: Vivek R

•12 мар. 2019 г.

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.

автор: Kohei K

•2 окт. 2020 г.

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 !

автор: Harsh S

•9 июня 2020 г.

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.

автор: Jatin k

•18 июня 2020 г.

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.

автор: Subham

•3 мар. 2019 г.

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.

автор: Martins R

•24 апр. 2019 г.

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.

автор: Manish S

•4 февр. 2020 г.

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.

автор: Emily C

•2 янв. 2020 г.

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!

автор: Vaibhav J

•5 июня 2019 г.

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.

автор: Issam B

•7 дек. 2020 г.

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.

автор: Anup

•21 мая 2020 г.

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.

автор: Ken P

•1 мая 2020 г.

Amazing Course by an amazing professor ! I am a Mechanical Engineer, and don't have any IT / Data experience. But even then how Professor Ng laid down ML foundations and took the course forward was very smooth. I am glad I took this course and I hope that the Professor keeps coming out with more advanced videos on ML. Thank you Andrew Ng for the months of work and dedication you put in for coming out with such a brilliant course for us students !

автор: Andrea R

•3 июля 2020 г.

Best online course I've ever taken so far, and it's free! Please keep it always free! ML is not my field but thanks to this wonderful course I now have a very good high-level grasp of the argument as well as some technical knowledge to start tackling real-word problems. Andrew Ng is an exceptional teacher. Assignments are a great way to commit theoretical concepts to memory. You will also learn the basics of Octave/Matlab which is useful per se.

автор: Miklós L

•15 янв. 2019 г.

Amazing class, great lecturer. A really good introduction and overview of machine learning concepts. It often skips the detailed mathematical background, to make it accessible for a wider audience, but I still found that enough information was given that allowed me to work out these details on my own. A lot of effort was put into creating the programming assignments, they provide a great hands-on experience with machine learning algorithms.

автор: Antonio R

•23 июля 2020 г.

Although this is an introductory Machine Learning course, it covers all the important aspects and all the common algorithms, the programming skill is not so necessary with Matlab, a production-ready software, or its free software clone Octave, even if it is needed GPU acceleration. This course was much easier than I expected, even considering the lack of unnecessary mathematical theory, it was teacher Andrew Ng who made it possible.

автор: Sparsh K

•23 авг. 2020 г.

Hello this is Sparsh kaushik and i just completed my ML course offered by Stanford.

To be honest this course offered exactly what i was looking, a proper Introduction into the world of Machine Learning.I really appreciate the efforts of prof Ng for making this course and all the mentors who guided me throughout the course.

Overall this course is a must if you are new to ML and want to dive into this beautiful word of data.

автор: Varadharaj P

•31 дек. 2020 г.

This is the most interesting course that I had ever taken. I want to thank my tutor Andrew Ng for teaching those wonderful things. This course even changed my career path and also made me superior to my teammates by knowing these cool stuff. I have also designed some applications using the concepts I had learned in this course. Lastly I am grateful to my tutor @Andrew_Ng for make profession life more fruitful.

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