With the rise of machine learning in finance, many investment banking professionals are looking to gain skills in this cutting-edge technology. Luckily, there are some excellent free machine learning courses available online that are perfect for learning the machine learning skills needed for investment banking. In this article, we will explore the best free options for machine learning courses on GitHub and PDF formats to level up your investment banking career.
Look for courses from top universities like MIT and Stanford
Some of the best free machine learning courses come from top universities like MIT, Stanford, and Harvard. For example, MIT’s course 6.S094: Deep Learning for Self-Driving Cars teaches the fundamentals of deep learning and how to apply algorithms to autonomous vehicle systems. Stanford’s CS229 Machine Learning course covers a wide range of machine learning topics including supervised learning, unsupervised learning, reinforcement learning, and more. The great thing is many top universities make their course materials available for free online.
Leverage GitHub repositories from machine learning experts
Beyond university courses, there are many talented machine learning engineers sharing their knowledge on GitHub. For instance, fast.ai founder Jeremy Howard has an excellent Practical Deep Learning for Coders course covering topics like computer vision, NLP, tabular data, and more. Sentdex creates accessible tutorials on YouTube and provides the accompanying code on GitHub. So search for top machine learning experts on GitHub to find well-annotated code examples and hands-on projects you can learn from.
Look for project-based courses that teach through implementation
The best way to learn machine learning is by doing. So prioritize courses structured as hands-on machine learning projects as opposed to just theory. For example, Coursera’s Guided Projects have short 2-hour courses leading you through implementing machine learning techniques step-by-step. GitHub hosts many machine learning projects as well. Implementing projects end-to-end will build your intuition and skills for applying machine learning in an investment banking context.
Learn from machine learning resources tailored to finance
For investment banking specifically, look for machine learning resources designed for finance. Udemy has affordable courses like Machine Learning A-ZTM: Hands-On Python & R In Data Science dedicated to using machine learning for trading, stock prediction, and modeling financial data. O’Reilly’s Machine Learning for Finance book also covers important algorithms like regression, classification, and neural networks with financial examples using Python.
Focus on fundamental algorithms like regression and neural networks
Make sure the courses teach the machine learning techniques most applicable to investment banking. Linear regression, logistic regression, decision trees, random forests, and neural networks are some of the most important foundational algorithms. Also look for resources covering time series forecasting, natural language processing (NLP), and reinforcement learning as those have many finance use cases. Mastering the fundamentals will give you the tools to tackle whatever machine learning problems you come across in banking.
With abundant free resources available online, investment banking professionals can develop in-demand machine learning skills without spending a dime. Prioritize hands-on courses in machine learning for finance from reputable platforms like edX, Coursera, and Udemy. Also leverage materials from top universities and experts shared on GitHub. Focus on building mastery in fundamental algorithms like regression and neural networks. Combine theory and hands-on projects to maximize learning.