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Guided Tour of Machine Learning in Finance

Approx. 24 hours to complete

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Course Summary

This course provides a guided tour of machine learning applications in finance, including predictive analytics and risk management. You will learn how to use machine learning algorithms to analyze financial data, make predictions, and optimize investment decisions.

Key Learning Points

Understand the fundamentals of machine learning and its applications in finance
Learn how to use machine learning algorithms for predictive analytics and risk management
Apply machine learning techniques to optimize investment decisions

Learning Outcomes

Ability to apply machine learning algorithms to financial data analysis
Understanding of predictive analytics and risk management in finance
Ability to optimize investment decisions using machine learning techniques

Prerequisites or good to have knowledge before taking this course

Basic knowledge of statistics and programming
Familiarity with financial concepts and terminology

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced
Guided

Similar Courses

Applied Data Science in Finance
Machine Learning for Trading
Financial Markets

Related Education Paths

Notable People in This Field

Andrew Ng
Yves Hilpisch
Kai-Fu Lee

Related Books

Description

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.

Outline

Artificial Intelligence & Machine Learning
Welcome Note
Specialization Objectives
Specialization Prerequisites
Artificial Intelligence and Machine Learning, Part I
Artificial Intelligence and Machine Learning, Part II
Machine Learning as a Foundation of Artificial Intelligence, Part I
Machine Learning as a Foundation of Artificial Intelligence, Part II
Machine Learning as a Foundation of Artificial Intelligence, Part III
Machine Learning in Finance vs Machine Learning in Tech, Part I
Machine Learning in Finance vs Machine Learning in Tech, Part II
Machine Learning in Finance vs Machine Learning in Tech, Part III
The Business of Artificial Intelligence
How AI and Automation Will Shape Finance in the Future
A. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapter 1
Module 1 Quiz

Mathematical Foundations of Machine Learning
Generalization and a Bias-Variance Tradeoff
The No Free Lunch Theorem
Overfitting and Model Capacity
Linear Regression
Regularization, Validation Set, and Hyper-parameters
Overview of the Supervised Machine Learning in Finance
I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, Chapters 4.5, 5.1, 5.2, 5.3, 5.4
Leo Breiman, “Statistical Modeling: The Two Cultures”
Jupyter Notebook FAQ
Module 2 Quiz

Introduction to Supervised Learning
DataFlow and TensorFlow
A First Demo of TensorFlow
Linear Regression in TensorFlow
Neural Networks
Gradient Descent Optimization
Gradient Descent for Neural Networks
Stochastic Gradient Descent
A.Geron, “Hands-On ML”, Chapter 9, Chapter 4 (Gradient Descent)
E. Fama and K. French, “Size and Book-to-Market Factors in Earnings and Returns”, Journal of Finance, vol. 50, no. 1 (1995), pp. 131-155.
J. Piotroski, “Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers”, Journal of Accounting Research, Vol. 38, Supplement: Studies on Accounting Information and the Economics of the Firm (2000), pp. 1-41
Jupyter Notebook FAQ
Module 3 Quiz

Supervised Learning in Finance
Regression and Equity Analysis
Fundamental Analysis
Machine Learning as Model Estimation
Maximum Likelihood Estimation
Probabilistic Classification Models
Logistic Regression for Modeling Bank Failures, Part I
Logistic Regression for Modeling Bank Failures, Part II
Logistic Regression for Modeling Bank Failures, Part III
Supervised Learning: Conclusion
C. Bishop, “Pattern Recognition and Machine Learning”, Chapters 4.1, 4.2, 4.3
A. Geron, “Hands-On ML”, Chapters 3, Chapter 4 (Logistic Regression)
Jupyter Notebook FAQ
Jupyter Notebook FAQ
Module 4 Quiz

Summary of User Reviews

Discover the world of machine learning for finance with Coursera's Guided Tour. This course has received positive reviews from users who found it to be a great introduction to the subject. Many appreciated the interactive nature of the course, which helped to solidify their understanding of the concepts.

Key Aspect Users Liked About This Course

interactive nature of the course

Pros from User Reviews

Easy to follow and understand
Great introduction to machine learning for finance
Interactive format helps solidify understanding of concepts
Excellent instructors with real-world experience

Cons from User Reviews

Some users felt the course was too basic
Limited coverage of certain topics
Not enough practical applications discussed
Some users found the course to be too theoretical
No hands-on coding exercises

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