Machine Learning for Finance in Python

Course Overview

This course provides an introduction to Machine Learning for Finance in Python. Participants will learn how to apply machine learning to financial data, investigate stock data and prepare it for use by machine learning algorithms. They will also learn how to use tree-based machine learning models to forecast stock price movements in the future, as well as how to use forest-based machine learning methods for regression and feature selection. Additionally, participants will learn how to normalise and scale data before using it in KNN and neural network methods. They will also learn how to use KNN and neural network regression to forecast a stock's price in the future (or any other regression problem). Finally, participants will learn how to plot and find optimal stock portfolios using modern portfolio theory (MPT) and the Sharpe ratio.

[Applications]
After this course, students can apply the knowledge they have gained to develop machine learning models for stock price forecasting, portfolio optimization, and other financial applications. They can also use tree-based and forest-based machine learning models to identify important features in financial data. Additionally, they can use KNN and neural network regression to forecast stock prices and other regression problems. Finally, they can use modern portfolio theory (MPT) and the Sharpe ratio to plot and find optimal stock portfolios.

[Career Path]
The recommended career path for learners of this course is a Machine Learning Engineer in the field of Finance. This job position involves developing and deploying machine learning models to solve financial problems. The Machine Learning Engineer will be responsible for researching, designing, and implementing machine learning algorithms to analyze financial data and make predictions. They will also be responsible for optimizing existing models and developing new ones.

The development trend for this job position is to focus on the use of deep learning and reinforcement learning algorithms to improve the accuracy and speed of financial predictions. Additionally, the Machine Learning Engineer will need to stay up to date with the latest advancements in the field of machine learning and finance, such as new algorithms and techniques. They will also need to be able to work with large datasets and be able to interpret and explain the results of their models.

[Education Path]
The recommended educational path for learners interested in Machine Learning for Finance in Python is a Bachelor's degree in Computer Science or a related field. This degree will provide learners with the foundational knowledge and skills needed to understand and apply machine learning algorithms to financial data.

The Bachelor's degree in Computer Science or a related field will cover topics such as programming, data structures, algorithms, software engineering, databases, operating systems, computer networks, and computer architecture. Additionally, learners will gain an understanding of machine learning algorithms and their applications in finance.

The development trend for this educational path is to focus on the application of machine learning algorithms to financial data. This includes topics such as portfolio optimization, stock market prediction, and risk management. Additionally, learners should be familiar with the latest technologies and tools used in machine learning, such as deep learning, natural language processing, and reinforcement learning.