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.
Generalization and a Bias-Variance Tradeoff
Loading...
Skills You'll Learn
Deep Learning, Artificial Neural Networks, Algorithms, Regression, Human Learning, Applied Machine Learning, Machine Learning Algorithms, Machine Learning, Finance
Reviews
3.8 (678 ratings)
- 5 stars42.03%
- 4 stars23.59%
- 3 stars14.01%
- 2 stars10.17%
- 1 star10.17%
KN
Jul 26, 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.
AB
May 28, 2018
Exceptional disposition and lucid explanations! Ideal for a Risk Management professional to sharpen machine learning skills!
From the lesson
Mathematical Foundations of Machine Learning
Taught By
Igor Halperin