The coding part could have been better explained and the reasoning for what is being done should be included in the coding videos.

Fantastic lectures, great first programming assignments with unfortunate tail quality of the programming assignments

The lectures were Ok and the course assignments were Ok as well, but they had very little to do with each other. The course and ideas have so much more potential than was provided with this class. It is very unfortunate.

Lectures are good. Exercises are confusing, kind of irrelevant to the lectures and do not have any information about the underlying data. Sample codes are useful though.

I read the reviews by others before submitting mine. I agree that (1) the lecture material is very informative and of good quality, (2) assignments are sometimes hard to follow, but I have seen much worse on coursera in other courses (from Columbia, for instance). (3) Some people complained about professor's accent, he does have a fairly prominent Russian accent, but everything is still very much understandable and you can't hold something like this against him. (4) Some people claim that although they "have a strong math background" the material in lectures is intentionally made sound hard. I have a math background too and found his explanations very intuitive. My undergraduate math treatment of related topics was much more rigorous that this. (5) Others complain about there being too much math/many equations in lectures. ML is about math, if this tiny bit of mathematical rigor is too much for you, you should not be doing ML. (6) To my surprise, some people are wondering who are the target audience. The professor clearly states that the audience for the class is people with ML background willing to learn some financial applications. This is not really to teach you ML from scratch, but to introduce ML professionals to the field of finance. In my opinion, the professor succeeds in this and gives a list of good references for ML theory too.

This is an excellent course bringing together machine learning and finance. The content and exercises are just nice as introduction to both subjects. The clarity of contents presented in relating these 2 are timely and commendable. The Jupyter notebooks were a little buggy with some annoying glitches in the beginning but things are all ok. The descriptions in Jupyter on what the students need to achieve probably need a bit of polish. Overall a 5-star. Great job to Professor Halperin and team.

I absolutely love the depth that this course goes into by providing in-depth reading materials and citing advanced sources in videos for further research. Although some other reviews say that the assignments are too hard and no guidance is given, I think this is an advantage because a lot more learning goes on. I've taken other courses where all that you have to do is fill in about 10 lines of code for the entire assignment after 10 paragraphs of explanation and it really kills the learning.

At first I was irritated that some of the material wasn't covered in class but when I read all of the recommended reading then it became more clear what to do. This course takes time and attention. Its not an introduction course, its more an an intermediate course. I was impressed with this course as it directly relates to applications in finance and helped me to see how to apply algorithms I already know to finance. It also gave me a bit more mathematical rigor.

Perfect courses with challenging assignments. Together with recommended references, I learned a lot in machine learning, both about algorithms itself and applications in finance. Through the course, I finally understand ML is NOT a black box, but an optimization methods based on probability theories.

I really love such research, and I will complete the whole specialization without doubt!

I liked this course. It extends your knowledge regarding such basic algorithms as linear/logistic regression, gives some useful practice with TensorFlow. But, I would definitely recommend everyone, who didn't understand the material go through it again and read recommended materials after each week. Otherwise, such lack of understanding will be like a snowball.

Except for a few issues with assignment submission the course material and exposition and recommended readings were excellent. As a disclaimer, I have taken non-financial ML courses in the past, though, so I do have some background knowledge on tensorflow. That might influence my opinion.

Excellent course, but be prepared for hard work. It's an intermediate level not an introductory course. It would be better if the assignments were better documented - it's true that we should get used to do our own research but that significantly increases the scheduled work load .

While this course gets a lot of negative comments due to the inconsistencies between the exercises and the actual material, it taught me a lot about the probabilistic models behind popular machine learning algorithms. Also learning to do things in tensorflow is a great bonus.

This is a great course, I really learned the topics. Some people has made bad comments regarding the programming assignments difficult. But really is this difficulty what help to go deeper in the topic and conect the theory with the practice. Excelent!

Well, the lessons are amazing. But the projects are very difficult and not so related to a better learning curve. Do a linear regression in 100 ways and thousands tools doesnt make difference. Could approach only one, but focused.

Really good content which is pretty focused and at the same time pretty generic. Totally perfect for someone who has python coding experience and some interest/experience in finance and ML. No prerequisites in ML/Finance required.

learn useful programming code for business related data but I am still a little bit confused about the mathematical and theoretical part like MLE, Prior, posteriors, and Merton model and so on. In short, it is a great class!

The course is challenging unlike other Coursera courses, you need to learn TensorFlow if you want to pass the programming assignments. Some out of course studying was involved to complete the assignments as well.

This course is a perfect introduction to machine learning applied to finance, which covers the essentialtopics that students must know to deepen their knowledge in this fascinating field.

Introduction of ML for Financial application with combination of Scikit learn, Statsmodels and Tensorflow with neuralnets made this class very interesting. Learned and Enjoyed lot.

Great course. but requires lot of patience. Uses lot of unnecessary symbols and equations to explain concepts. Overall it is a good overview of the big picture of ML in finance.

Excellent! it is very wider and get to be so clear at the same time. It was an amazing experience specially because I am returning back to Coursera courses.

I really recommend this course,some courses your learn just tell you what to do,but when you finish this one,you will want to dig deeper.wonderful!

Exceptional disposition and lucid explanations! Ideal for a Risk Management professional to sharpen machine learning skills!

Leans heavily on explaining differences between tech and finance applications of ML, but still great!