What Does MVP Stand For? It’s Not What You Think.
October 7, 2024
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This course is part of Machine Learning and Reinforcement Learning in Finance Specialization
Instructor: Igor Halperin
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(674 reviews)
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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.
11 videos3 readings1 assignment
6 videos3 readings1 assignment1 programming assignment1 ungraded lab
7 videos4 readings1 assignment1 programming assignment1 ungraded lab
9 videos4 readings1 assignment2 programming assignments2 ungraded labs
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Reviewed on Jul 25, 2022
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.
Reviewed on Aug 23, 2019
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.
Reviewed on Apr 17, 2021
Great overview. Please provide more code examples as homework require a lot more than what the class covers!
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