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Guided Tour of Machine Learning in Finance

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Online Course Highlights
  • New York University via Coursera
  • Learn for FREE, Up-gradable
  • 20 hours of effort required
  • 1,919 already enrolled!
  • 4.7 ★★★★★ (23 Ratings)
  • Skill Level: Intermediate
  • Language: English

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.

If you’re curious about how effective this program really is, be sure to read our Machine Learning with Python Course Review.

This is the fourth course in the Information Visualization Specialization. The course expects you to have some basic knowledge of programming as well as some basic visualization skills (as those introduced in the first course of the specialization)

Syllabus

WEEK 1: Artificial Intelligence & Machine Learning

WEEK 2: Mathematical Foundations of Machine Learning

WEEK 3: Introduction to Supervised Learning

WEEK 4: Supervised Learning in Finance

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