Explore

Fundamentals of Machine Learning in Finance

Approx. 18 hours to complete

Save Course

Go to Course

Course Summary

This course provides an introduction to the fundamentals of machine learning in finance. Students will learn how to apply machine learning techniques to financial data to make predictions and decisions.

Key Learning Points

Understand the basics of machine learning and how it can be applied in finance
Learn how to use Python and the scikit-learn library for machine learning
Explore common machine learning techniques such as regression, classification, and clustering

Learning Outcomes

Understand the basics of machine learning and how it can be applied in finance
Be able to use Python and the scikit-learn library for machine learning
Understand common machine learning techniques such as regression, classification, and clustering

Prerequisites or good to have knowledge before taking this course

Basic knowledge of Python programming
Basic knowledge of finance and financial data

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced
Video lectures

Similar Courses

Applied Data Science with Python
Data Science Essentials

Related Education Paths

Notable People in This Field

Andrew Ng
Kaggle

Related Books

Description

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.

A learner with some or no previous knowledge of Machine Learning (ML) will get to know main algorithms of Supervised and Unsupervised Learning, and Reinforcement Learning, and will be able to use ML open source Python packages to design, test, and implement ML algorithms in Finance. Fundamentals of Machine Learning in Finance will provide more at-depth view of supervised, unsupervised, and reinforcement learning, and end up in a project on using unsupervised learning for implementing a simple portfolio trading strategy. 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

Fundamentals of Supervised Learning in Finance
What is Machine Learning in Finance?
Introduction to Fundamentals of Machine Learning in Finance
Support Vector Machines, Part 1
Support Vector Machines, Part 2
SVM. The Kernel Trick
Example: SVM for Prediction of Credit Spreads
Tree Methods. CART Trees
Tree Methods: Random Forests
Tree Methods: Boosting
A. Smola and B. Scholkopf, “A Tutorial on Support Vector Regression”, Statistics and Computing, vol. 14, pp. 199-229, 2004
A. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapters 6 & 7
K. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2009, Chapter 16.4
Jupyter Notebook FAQ

Core Concepts of Unsupervised Learning, PCA & Dimensionality Reduction
Core Concepts of UL
PCA for Stock Returns, Part 1
PCA for Stock Returns, Part 2
Dimension Reduction with PCA
Dimension Reduction with tSNE
Dimension Reduction with Autoencoders
C. Bishop, “Pattern Recognition and Machine Learning”, Chapter 12.1
A. Geron, “Hands-On ML”, Chapters 8 & 15
Jupyter Notebook FAQ

Data Visualization & Clustering
UL. Clustering Algorithms
UL. K-clustering
UL. K-means Neural Algorithm
UL. Hierarchical Clustering Algorithms
UL. Clustering and Estimation of Equity Correlation Matrix
UL. Minimum Spanning Trees, Kruskal Algorithm
UL. Probabilistic Clustering
C. Bishop, “Pattern Recognition and Machine Learning”, Clustering and EM: Chapter 9
G. Bonanno et. al. “Networks of equities in financial markets”, The European Physical Journal B, vol. 38, issue 2, pp. 363-371 (2004)
Jupyter Notebook FAQ

Sequence Modeling and Reinforcement Learning
SM. Latent Variables
Sequence Modeling
SM. Latent Variables for Sequences
SM. State-Space Models
SM. Hidden Markov Models
Neural Architecture for Sequential Data
RL. Introduction
RL. Core Ideas
Markov Decision Process and RL
RL. Bellman Equation
RL and Inverse Reinforcement Learning
C. Bishop, “Pattern Recognition and Machine Learning”, Chapter 13
S. Marsland, “Machine Learning: an Algorithmic Perspective” (Chapman & Hall 2009), Chapter 13
Jupyter Notebook FAQ

Summary of User Reviews

Key Aspect Users Liked About This Course

The course covers a wide range of topics in machine learning applied to finance, which helps learners gain a comprehensive understanding of the subject.

Pros from User Reviews

The instructor is knowledgeable and engaging, making the course material easy to follow.
The course offers hands-on experience with real-world financial data, which is valuable for practical applications.
The assignments and quizzes are challenging but helpful in reinforcing the concepts learned in class.
The course provides a good balance between theory and practical applications of machine learning in finance.
The course is well-structured and easy to navigate, with clear instructions and explanations.

Cons from User Reviews

The course requires a solid background in finance and programming, which may be challenging for beginners.
The course does not cover some advanced topics in machine learning, which may disappoint more experienced learners.
The course is time-consuming, with a lot of material to cover in a short amount of time.
The course may not be suitable for learners who prefer a more traditional lecture-based format.

Recommended for you

R Crash Course - Introduction to R / RStudio / R-programming

R Crash Course - a short and concise introduction to R and RStudio, R-programming for the Beginners What will you learn in this course:...

Save Course

Data Science: Machine Learning algorithms in Matlab

A-Z Guide to Implementing Classic Machine Learning Algorithms From Scratch and with Matlab and maths. In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning....

Save Course

Fintech: Overview of the Fintech Sector

This course provides a review of the most recent technological advances that are radically changing the financial services industry. The course is structured in three modules....

Save Course

Introduction to Personal Finance

This course is a general overview of a variety of personal finance topics - including budgeting, the importance of your credit score, cash flow, setting financial goals, and taxes....

Save Course