With the rise of artificial intelligence and machine learning, investment banks have been increasingly adopting these technologies to improve their trading strategies, risk management, client targeting, and more. For those looking to break into investment banking or advance their careers, having machine learning skills is becoming more and more important. Fortunately, there are many free online resources for learning machine learning, from courses and textbooks to hands-on projects and coding challenges. In this article, we will recommend the best free machine learning resources for investment bankers and highlight key skills like Python programming, data analysis, and model building.
Python machine learning tutorials teach coding skills
Python has become the go-to language for machine learning due to its ease of use and extensive libraries. For investment bankers new to coding, Python machine learning tutorials like Scikit-Learn, TensorFlow, and PyTorch tutorials can teach data manipulation, model building, and other core skills. Kaggle’s Python tutorial is very hands-on, taking you from basic syntax to advanced techniques. Sentdex and Corey Schafer have excellent YouTube tutorials covering everything from data visualization to neural networks. For a structured curriculum, IBM’s Python for Data Science course provides foundation knowledge. Books like ‘Python Machine Learning’ and ‘Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow’ are also great references.
Data analysis courses hone quantitative skills
Since machine learning relies heavily on working with data, strengthening data analysis skills is crucial. Coursera has many excellent, free data analysis courses like IBM’s Data Analysis with Python and Data Visualization with Python. Udemy courses like Data Analysis with Pandas cover retrieving, cleaning, analyzing, and visualizing data in Python. Books like ‘An Introduction to Statistical Learning’ provide a thorough treatment of supervised and unsupervised learning techniques. Kaggle also has structured learning paths and hands-on data analysis competitions to practice real-world skills.
Advanced machine learning programs offer certification
For investment bankers looking to seriously upskill, structured machine learning programs that offer certification provide comprehensive training. Coursera’s Machine Learning by Stanford Professor Andrew Ng is the most popular, teaching fundamentals like regression, classification, neural networks, and more. Udacity’s Machine Learning Engineer Nanodegree covers unsupervised learning, reinforcement learning, NLP, and other advanced topics. These programs take significant commitment, but offer opportunity to work on real-world projects, receive feedback, and showcase a certificate for career advancement.
Books and datasets provide materials for self-study
For self-driven learners, machine learning books and datasets allow practicing skills independently. Classic books like ‘Machine Learning’ by Tom Mitchell, ‘Pattern Recognition and Machine Learning’ by Christopher Bishop, and ‘The Elements of Statistical Learning’ cover theory extensively. Resources like Kaggle datasets, UCI Machine Learning Repository, and Google’s BigQuery Public Datasets provide abundant data for projects. Building a machine learning model and portfolio of projects to showcase will be important for getting hired as a self-taught practitioner.
Python programming, data skills, and hands-on practice are crucial for learning machine learning. Free online courses, textbooks, datasets, and projects provide ample resources for investment bankers to gain these in-demand skills.