With the radical growth of data science, machine learning, and cloud computing, the employment trend in the finance industry has been proliferating. It is reported that financial companies, including banks, employ around 60% of all professionals skilled in Machine Learning and AI systems.
Machine Learning is critical for financial institutions to stay competitive and thrive. Companies are constantly enlarging their use of machine learning to optimize customer experience and back-office operations. There are various kinds of Machine Learning jobs needing specialty skills and experience. Machine Learning jobs in Finance seek applicants with a solid background in mathematics, experience in machine learning, deep learning, neural networks, and statistical programming skills in Java, Python, R and Scala.
Most finance companies expect professionals to have a functional knowledge of computer vision, probabilistic graphical models, reinforcement learning, and natural language processing. It also helps tremendously to have some data engineering skills and high-level familiarity with benchmarking, parallel computing, and distributed computing.
ML Engineers working in the finance industry are specialists. The road, however, to becoming a specialist isn’t as straightforward. It involves techniques that can be exceedingly challenging to understand without an effective teacher.
This guide will help you identify which learning track best meets your needs. Let’s compare top resources based on technical mentorship, training content, reputation, and more.
5 Best Machine Learning for Finance and Trading Courses
While some educators are known for their long and theoretical approach to study, these learning programs are focused on hands-on training with the tools, problems, and inner workings of a typical ML role in finance.
We’re assuming you are well-versed in programming and you have a background in mathematics and basic familiarity with Machine Learning concepts.
Artificial Intelligence for Trading
AI for Quantitative Trading is a job-focused learning program developed by Udacity and WorldQuant to equip you with the most coveted data skills to pursue a career in AI quant trading.
The curriculum used by this program focuses heavily on allowing you to study, analyze, and tackle real-life scenarios with hands-on deep learning projects and career-related coursework to master AI algorithms for trading in Python. This approach aims to establish you with the skills and a strong technical foundation in quantitative analysis, programming, statistical modeling, and AI applications in quantitative finance.
Career outcome
This job-focused Nano-degree program will help you build an impressive portfolio of real-world projects and prepare you for a rewarding career at hedge funds, investment banks, and FinTech startups. You will also be assigned a technical mentor, with whom you’ll meet whenever you need technical guidance.
Objectives
The program comprises learning content and curriculum to support eight (8) projects. Through on-demand videos, in-depth articles, and technical guidance from industry experts, you'll build skills in quantitative analysis, data processing, trading signal generation, and portfolio management.
Is it right for you?
This program is practically suitable for learners with Python programming skills and familiarity with statistics, linear algebra and calculus. It is aimed at professionals in adjacent fields like data science, deep learning, computer vision, reinforcement learning, and more. It is popular amongst machine learning professional who want to upskill as well as data scientists who want to bootstrap their quantitative skill set.
Artificial Intelligence for Trading — Udacity
Machine Learning for Finance with Python
DataCamp is a platform offering both data science and machine learning courses for beginners. Its Machine Learning for Finance in Python course is a good introduction to learn how to build models and predict stock data values using linear models, decision trees, random forests, and neural networks.
The best part is that you’ll gain hands-on experience in using machine learning algorithms and tree-based machine learning models to predict future values of a stock's price.
Career outcome
It hits the essential concepts and approaches Machine Learning from a business perspective to equip learners with the scientific knowledge of how to make predictions using ML techniques to make stock trading strategies profitable.
Objectives
This interactive program helps learners understand how to prepare features for linear models, xgboost models, and neural network models. You will learn the key concepts of Time series data and understand how to use linear models, decision trees, random forests, and neural networks to predict the future price of stocks.
Is it right for you?
This program is suitable for Python programmers with a working knowledge of supervised learning with scikit-learn. It is good for individuals with a limited background in reinforcement learning. By the end, you'll have skills to use forest-based machine learning methods for regression and feature selection.
Machine Learning for Finance in Python
Fundamentals of Machine Learning in Finance
NYU is a world-leading university that offers an extensive array of machine learning courses. Its Fundamentals of Machine Learning in Finance is a comprehensive course that introduces learners to essential concepts.
The curriculum covers supervised and unsupervised learning, reinforcement learning, and more. It's filled with hands-on practice projects and exercises to help strengthen your Python skills to implement ML algorithms.
Career outcome
You will gain skills to solve practical Machine Learning problems and understand the fundamental concepts using open-source ML algorithms and tools that fit with financial data. This program will increase your understanding of how probabilistic models are connected to Machine Learning models and prepare you for learning advanced topics.
Objectives
The interactive course is primarily self-paced for learning independently from pre-recorded lectures, exercises and readings to help you become acquainted with the common ML approaches most appropriate for solving actual problems.
Is it right for you?
This intermediate-level course is good for Python programmers with a functional knowledge of numpy, pandas, and jupyter notebook, linear algebra, statistics, and calculus. By the end, you'll be able to solve real-world problems and successfully implement ML solutions.
Fundamentals of Machine Learning in Finance — NYU
Machine Learning for Trading Specialization
Google Cloud and the New York Institute of Finance provide one of the longest-running Machine Learning Specialization for Trading, with multiple labs and projects designed to mimic real-life professional scenarios to structure and apply techniques used in reinforcement learning (RL) strategies for trading.
This specialization presents a great approach to learn by doing, and the courses are designed for independent individuals who enjoy learning through the material at their own pace.
Career outcome
You will learn to create quantitative strategies and algorithmic trading techniques using Python. These skills are indispensable in finance technology and you can showcase these skills to create and enhance quantitative trading strategies with machine learning and deep learning in your ML/ Data Science Portfolio for job opportunities.
Objectives
This specialization covers the most important concepts of trading and Cloud Machine Learning with the Google Cloud Platform. The content is developed to help machine learning professionals upgrade their technical skills to create quantitative and algorithmic trading strategies.
Is it right for you?
This specialization program is suitable for those who understand the foundations of Machine Learning at an intermediate level and possess the knowledge of financial markets. You should have the functional knowledge of Python programming and a solid background in mathematics and statistics to work ML libraries such as scikit-learn, StatsModels, and Pandas. Experience with SQL will be helpful to get ahead faster.
Machine Learning for Trading Specialization — GCP
Machine Learning and Reinforcement Learning in Finance Specialization
NYU’s Specialization courses in Machine Learning and Reinforcement Learning is for individuals looking to learn the applications of Machine Learning and Deep Learning, and advanced techniques to solve practical problems in finance.
This program is broad and gives students a taste of the various elements to use machine learning techniques, such as regression and classification. It goes over problem identification with deep learning techniques and architectures and their applications in finance.
Career Outcome
The program is dedicated to equipping machine learning novices, analysts and data scientists with a deeper understanding of supervised learning (regression and classification) and unsupervised learning, and the useful applications of both. After completing the fundamentals, you'll dig deeper to construct a problem-solving map with deep learning and neural networks for financial engineering.
Objectives
NYU offers four courses in this specialization covering important areas of reinforcement learning, and advanced methods of reinforcement learning in finance. You'll get a deep understanding of the various machine learning and deep learning approaches in financial technology.
Is it right for you?
This program is intended for learners who have a knowledge of probability and statistics, linear regression, calculus; linear algebra. You'll also need a basic familiarity with machine learning to understand how Machine Learning and Reinforcement Learning can improve practice in financial engineering.
Machine Learning and Reinforcement Learning in Finance Specialization — NYU
Adjunctive Resources
- Linear Algebra for Machine Learning
- Mathematics for Machine Learning
- Probability and Statistics for Machine Learning
- High-RIO Machine Learning Certification
- TensorFlow Courses for Machine Learning
FAQs — Learning Machine Learning and AI for Trading and Finance
Read on for answers to frequently asked questions about how to launch a Machine Learning career in finance.
How Long Does it Take to Learn Machine Learning for Finance?
Machine Learning engineers in finance typically hold Master degrees in STEM. Most enroll in online learning programs to build the skills they need to get hired. This type of approach will typically require weeks or months of study, depending on your experience-level, schedule, pace, and learning style. Self-learning without technical mentorship will probably take longer, as you’ll need to build a custom curriculum, find answers to your questions, and separate the wheat from the chaff when evaluating resources, projects, and career guides.
Which AI Course is Best for Trading?
Not all AI courses for trading are made the same. Before enrolling for a course, look into its curriculum. And does it offer technical mentorship? The existence of a career service and mentorship signals confidence in the program and its ability to help graduates land a proper job.
How Do You Choose a Machine Learning for Finance Course?
It’s one thing to learn all the ML skills for finance needed to do the work of a Financial engineer; it’s another to land a job. Look for programs that will support you in your academic journey, provide instructor support and career advice. Does a program include technical mentorship or career counseling? Is portfolio preparation included? Will the program give you a competitive edge? These are all things to consider when choosing a Machine Learning for Finance Course.
What’s the Best Reinforcement Learning Course in Finance?
The best Reinforcement Learning course for finance is the one that meets your needs. For example, if you’re a complete beginner who is trying to learn the fundamentals of machine learning and polish your skills in mathematics and statistics, then it's better to consider learning corequisites first. But if you’re a seasoned professional looking to master reinforcement learning and develop ML solutions for trading that result in accurate predictions, consider any of the five learning tracks in this guide.
CLOSING NOTE
Machine learning in the finance industry is truly revolutionary, making impossible things possible. There are a plethora of ways to receive a ML education, but for finance and trading, the high-quality resources are few. These programs will help you learn the tenets of Machine Learning for Finance and Trading to become a specialist.