Uses both Python and R. It will probably expand your mind a few IQ points. Posting solutions to Coursera assignments goes against the Coursera honor code. Some people who had not done either subject for some time did need to spend some time refreshing their knowledge. Estimated timeline of ten weeks. Unlike some other poorly-thought-out MOOC where you waste time looking for information or confused about what is expected, this class is extremely well organized and presented in a straightforward, humble manner.

Doing the assignment itself is critical to the educational experience of truly understanding that topic, reading a solution robs you of that. Professor Ng was very careful to present the material without much math — impressive to say the least. If you know little or nothing about Machine Learning, it will give you a solid foundation. In fact, the entire Udacity environment is in line with industry best practices and students who learn it will be well equipped in the job market. Just to get some very unscientific empirical data, I added the certificate to my LinkedIn profile just over a week ago.

Some thoughts on the Coursera Deep Learning Specialization

Attendance at these sections is optional. ML for the people! TeMPOraL 8 months ago Not if that thing goes against the more fundamental principle of sharing knowledge. You are strongly encouraged to answer other students’ questions when you know the answer. Courseraa props to the publisher of these. Here is a succinct description:. It’s bad enough with the utterly cpursera white board problems. At the start I will have no clue so I just check the solution.


In real life often there are no answers to copy from. I have maybe spent hours on this class hoping to get something real out of it. Taught by Andrew Ng.

Some thoughts on the Coursera Deep Learning Specialization

Courserw top-ranked courses below also provide gentle calculus and linear algebra refreshers and highlight the aspects most relevant to machine learning for those less familiar. However, I found this to be a strength. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition.

Released init covers all aspects of the machine learning workflow. The course is very mll and you can build very useful systems just based on the material presented in the course. So the “central objective” of that problem is not important?

Answers to commonly asked questions and clarifications to the homeworks will be posted on the FAQ. With such a high dollar amount, however, signing up for the Nanodegree program is obviously a much bigger consideration. I counted at least seven different people lecturing throughout the program.

Great course, highly recommend to anybody who is interested in data. Throughout the course, he keeps telling students not to worry about the math, and spoon feeding equations to…. All I got is confusion and a better idea about the topic in general.


coursera ml homework

The exercises involve mostly copying and pasting, rather than writing entire scripts. A version of the course also exists.

Solutions to Machine Learning Programming Assignments

Coding assignments are easy and most of the code is just ready to be filled. A graduate machine learning course. Explanation of the machine learning workflow. Unsupervised Learning in R DataCamp: The course is well structured and well taught by the Prof. Email required Address never made public. Coursega, your mileage may vary.

coursera ml homework

From the fact that 64 people upvoted this post, I assume I’m in for some downvotes but what’s right is ho,ework. Statistical Learning Stanford University: Questions in the discussion forum are answered instead by “Community TA’s”, that is, volunteers who took earlier sessions of the course.

His lectures are extraordinarily well-organized, thoughtful, and clear.

coursera ml homework