The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance.
This course is part of the Machine Learning and Reinforcement Learning in Finance Specialization
About this Course
Syllabus - What you will learn from this course
Fundamentals of Supervised Learning in Finance
Core Concepts of Unsupervised Learning, PCA & Dimensionality Reduction
Data Visualization & Clustering
Sequence Modeling and Reinforcement Learning
- 5 stars45.17%
- 4 stars20.56%
- 3 stars14.33%
- 2 stars5.60%
- 1 star14.33%
TOP REVIEWS FROM FUNDAMENTALS OF MACHINE LEARNING IN FINANCE
This is a great course, I strongly recommend. However, the assignments take a while to finish.
Furthered my understanding of how probabilistic models are connected to Machine Learning models. Very happy with the content in this course.
So far so good. The lecturer refers to projects of which some weren't covered in this course. So a little confusing. Takes lots of googling to finish this course.
Great course which covers both theories as well as practical skills in the real implementations in the financial world.
About the Machine Learning and Reinforcement Learning in Finance Specialization
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