Igor Halperin
This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance. The goal of Guided Tour of Machine Learning in Finance is to get a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to. The course is designed for three categories of students: Practitioners working at financial institutions such as banks, asset management firms or hedge funds Individuals interested in applications of ML for personal day trading Current full–time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory and basic calculus is necessary to complete assignments in this course.
Instructor Details
Courses : 2
Specification: Guided Tour of Machine Learning in Finance
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50 reviews for Guided Tour of Machine Learning in Finance
Add a review
| Price | Free |
|---|---|
| Provider | |
| Duration | 21 hours |
| Year | 2018 |
| Level | Intermediate |
| Language | English ... |
| Certificate | Yes |
| Quizzes | Yes |
FREE
Takayuki K –
One of assignments was hard. Explanation by lecturer was very easy to understand and appropriate long.
Juan A S –
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.
Dawid L –
Terrible. For the first time in long time I felt such abandoned. No support. Notebooks written sloppy with plenty of copy paste and no fixing. Thought more of the lecturer as well but videos feel like he’s just coming up with the material. Having strong mathematical background I felt that the lecturer is intentionally making simple things sound hard. I’m left with deep sense of wasted time. Leaving Coursera and never coming back.
Teemu P –
Do not take this course before you review week 2,3 and 4 coding assignments which are wholly disconnected and arbitrary guesswork assignments where your task is to fill in missing pieces of code without any guidance or support. In its current stage the course is inaccessible to all but most tenacious learners with significant python and scikit experience.
lcy9086 –
Not an introductory level course. If you are new to machine learning, I would suggest taking Andrew Ng’s course…..However some materials in this course are somewhat deep and rewarding if you have already got the basis.. The programming assignment is somehow painful and literally no introduction and demonstration of tensorflow is provided….. You need to do the reading and search the forum to get help to do the assignment
Debasish K –
Good because it gives a high level good overview of ML in Finance, SVM and Tensorflow. However, Some examples are very easy and some have been made difficult by providing no references. Tobit regression was very vague. No links to proper reference. Neural Network was the example from Geron’s Handbook but there were errors in the custom function that was defined. More mathematical depth is required.
Eduardo C –
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.
Ronald B –
The assignments of the last week were poorly planned, almost impossible to understand.
Swaminathan S –
Excellent. I picked up quite a bit of ML as applied to finance through this fast paced course.
Amir T –
The teaching quality is poor and lacks practical examples. It is too technical, which you don’t expect for this kind of courses. The mathematics were presented poorly and sometimes without context.
Yi B –
The course is not mature enough. If someone wants to learn machine learning in finance with efficiency and practicality, he or she should consider other options instead of this specialization/course.
Christophe O –
Very Difficult Impossible to succeed without very strong prior experience. Would deserve more guidelines
John G S –
I rate the lectures and the lecture material a 5; however, the exercises are poorly documented and prepared and there is zero presence on the Forums from any of the TA’s. The exercises, Forum and lack of TA’s I rate a 1. Thus the 3 rating.
Amro T –
This course is more of mathematical introduction to machine learning than actual practical machine learning tips and tricks course. Math is definitely crucial but the way it was conveyed was not really good. I would have provided a refresher week just in math to refresh the students before jumping into the mathematics in the course. In the notebooks, there is a lot that was missing. Because I was already familiar with the material and I used TensorFlow, Numpy, Sklearn and statsmodels before and built several models with them before, I was able to navigate through. But if I was a totally new student, I would have a very hard time going through those notebooks. A couple of good notes, Please try to summarize all the important equations into a PDF file either for the entire course or per week to be as a reference when needed.
Aydar A –
To much math in lectures, assignments are not coherent and complicated, im not sure that i need tensorflow from scratch to work with finance(Keras fits better)
Kiran –
Good lectures Irrelevant assignments No help on forum Don’t take this as a paid course to pass Just take this as an audit course
Nayan a –
Lectures are mostly short review of the topic. So you should know topics beforehand or supplement it with readings. Problems are great, you cannot solve it unless you understand the concept properly, so that good point.
Maksim G –
Good material but assignments explanation were too sparse and even expectation of material not covered in videos or readings (example is Tobit regression in week 4).
Omar E O F –
Very goo lectures, but assessment exercises are not well defined. Examples not shown in lectures. Not enough briefing for starting exercises. No active forum for discussion.
Ishrit T –
A more detailed introduction and guide to python for machine learning would have made this course one of the best out there
Noordeen m –
was good but expect alitle explanation on the finance stuff
Hrishikesh A R –
Objectives of assignments are not clear. The instructions provided in assignments are not clear. Tensorflow should be taught extensively because most of the students are facing problems in same.
Angelo J I T –
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.
Sudipto M –
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.
ALI R –
The course material are presented sparsely despite my initial expectation which may be formed by Andrew Ng in his ML course. Anyway I believe it is a good roadmap for learners of ML in finance and also for me to find and I should be grateful of the Coursera.
Krishna D –
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.
Vincent L –
extremely hard to follow, but better than when it originally came out. I had signed up after numerous ML courses and tried to skip to the later courses in this specialization. I got stuck trying to implement some crazy equations. I’m ok with looking up api methods, but the need to look out for reshaping is troublesome because it’s inconsistent throughout the course. Overall, hard to follow.
David W –
Leans heavily on explaining differences between tech and finance applications of ML, but still great!
Sridhar S –
Good Lectures and Presentations. However, there are gaps in the theoretical explanations. The assignments and the Final Project requires considerable learning from the resources. Considerable portion of learning is achieved by completing them.
Zhen C –
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.
Mohamed H a e r –
thanks coursera for this amazing course
Chris S –
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.
Frederic B –
Fantastic lectures, great first programming assignments with unfortunate tail quality of the programming assignments
Mayank J –
The coding part could have been better explained and the reasoning for what is being done should be included in the coding videos.
Serg D –
This course is highly academic and has nothing to do with the finance. The only realistic dataset used was for the final project. No resources provided, just names of articles and book chapters. Where am i supposed to get them from? The course does not have the practical part at all. It goes like this: you get 1 hour of videos with formulas and then supposed to write code. HOW????!
Benny P –
This course has been informative, and extremely FUN! This is not to say that it’s perfect, in fact as others say the assignments are quite challenging because there’s little introduction to the problem/solution being asked. But that’s exactly where the fun is! You need to search for the information yourself to solve the problem, much like in the real world. In fact I took another course on TensorFlow in the middle of this course to finish the assignment. But I can imagine this would be frustrating for those with less background on ML or programming, or people who expect everything to be presented smoothly for them.
Marlon F –
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.
Mike S –
The lectures were very good, but the assignments lacked supporting material. Also, most of the further reading was behind a paywall or the links had been removed.
Roland E –
The assignments and project are very briefly explained. It took me a lot of unnecessary time to figure out what I was supposed to do. Also the discussion forum is inactive and I have a feeling many leave after seeing not anyone respond to their questions. I think there should be one or two dedicated support answering questions at least within 3 days. The level of the course in general is pretty high, definitely not beginners level, which is fine I guess, but I do find the lectures are at times going very quick and at times overcomplicate. I would prefer an example to start simple and from there to build for a more complex situation. (For example start the bank failure with say 3 main features and show how you can decide to add another one by showing its impact through deviance and multicollinearity and show how you can then decide to add this new feature or not.)
Fabien N –
Actually I was finding that course amazing at first, but I gradually became very upset. The notebooks are way too high level and not self explanatory. The teacher seems amazing by his knowledge, but one are left with the notebooks without knowing what to do, and the lectures only partially help to solve the problems. A lot of search online needs to be done and I don’t think that is the spirit of Coursera courses. I was planning to pay for the whole specialization but unfortunately I will have to give up on this course that was very motivating at first…
Muntu –
Excellent Course, Well presented
Ehsan F –
It is neither good for the beginners nor for advanced users. Specially on the finance side it’s almost useless. People don’t come here so you send them to read several different books. They come here so you teach them what they would find in those books. That’s the actual added value and the service you are supposed to provide.
Hongsun K –
Great general overview of machine learning. I think the course can be re organized to incorporate some of the theory and some coding tips as well, however.
Vasco C –
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 .
Hussein H –
The name of the course is what caused me to purchase, I was super excited for this course up until i reached the coding assignment. The instructions we practically not succinct whatsoever and i literally had no clue what it was asking and how to even start. From the discussions and reviews it appears alot of people have this same issue.
Mohammed B –
Great course, but the coding projects are sometime hard to understand
Fred U –
Great lectures. Homework is not trivial: it requires web searches and significantly more perseverance than, say, Andrew Ng’s courses. Only 4 stars because I didn’t see any recent signs of active support in the Forums.
Gayatri G L –
Learned ML concepts and algorithms to be used in financial work.
Luis S M –
The lectures, as well as the quizzes, are great and coherent. However, the practical assignments, which are supposed to be the moment of cross checking your level of comprehension of the learned topics are rather frustrating. I believe it would be of great help to future course takers to clearly state your expectations (e.g. through more detailed exercise descriptions) and introducing vital concepts before requiring their use.
Chazz E –
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.