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Guided Tour of Machine Learning in Finance

Machine Learning and Reinforcement Learning in Finance,

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.

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Rating 3.0 based on 104 ratings
Length 5 weeks
Starts Oct 19 (last week)
Cost $49
From New York University Tandon School of Engineering, New York University via Coursera
Instructor Igor Halperin
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Algorithms Machine Learning

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What people are saying

machine learning

A more detailed introduction and guide to python for machine learning would have made this course one of the best out there Very goo lectures, but assessment exercises are not well defined.

Good lecturesIrrelevant assignmentsNo help on forumDon't take this as a paid course to passJust take this as an audit course 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) This course is more of mathematical introduction to machine learning than actual practical machine learning tips and tricks 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 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.

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.

Potentially great course with bridges technology (machine learning methods) and application (finance), but as for now it is really rough around the edges.

Excellent overview of machine learning in finance I liked this course.

The suggested book "Hands-On Machine Learning with Scikit-Learn & TensorFlow" is a very good resource.

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programming assignments

The positive experience is totally ruined by the quality of programming assignments though.

The course content is okay, but the programming assignments are not well designed.

Some people has made bad comments regarding the programming assignments difficult.

The subject material seems extremely interesting, and I couldn't wait to go through the course, but the graded programming assignments are terrible.

The programming assignments are left almost completely to the students guessing what they're suppose to do with little direction.

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hard to understand

Not very related to finance plus most of the tasks are easy to complete, but hard to understand what needs to be done.

It's unclear and it's very hard to understand what is asked and how it would be graded.

Some content are hard to understand what it wants me to do.

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grading system

The automatic grading system worked without incident once I figured it out and did not crash on me.

The automatic grading system does not work.

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overview of ml

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.

Good overview of ML and some basic applications to finance.

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complete the assignments

The material required to complete the assignments is mostly not covered in the lectures.

The Lectures and given readings are very useful and it is required to read them to complete the assignments which will otherwise be difficult horrendous on all fronts.

Careers

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Research Scientist-Machine Learning $55k

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Software Engineer (Machine Learning) $116k

Applied Scientist, Machine Learning $130k

Autonomy and Machine Learning Solutions Architect $131k

Applied Scientist - Machine Learning -... $136k

RESEARCH SCIENTIST (MACHINE LEARNING) $147k

Machine Learning Engineer 2 $161k

Machine Learning Scientist Manager $170k

Machine Learning Scientist, Personalization $213k

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Rating 3.0 based on 104 ratings
Length 5 weeks
Starts Oct 19 (last week)
Cost $49
From New York University Tandon School of Engineering, New York University via Coursera
Instructor Igor Halperin
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Algorithms Machine Learning

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