Learn Machine Learning for Finance (Reinforcement Learning and Algorithmic Trading)

The finance sector has seen a steep rise in the use cases of Machine Learning applications to advance better outcomes.

Learn Machine Learning for Finance (Reinforcement Learning and Algorithmic Trading)
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Machine Learning for finance offers enormous potential and it also involves techniques that can be exceedingly challenging to understand without an effective teacher.

Ever since the radical growth of data science and machine learning, the employment trend in finance is abounding.

Financial Institutions and Fintech startups are banking heavily on Artificial Intelligence — and they’re creating more and more tech-savvy jobs to accelerate the growth.

Currently, there are thousands of unique job level postings in the Finance Industry and the top job titles are;

  • Machine Learning Engineers
  • Data Scientists & Engineers
  • Experience Designers
  • AI Backend Engineers
  • Data Engineers

We know the options out there, and the skills needed for learners to understand quantitative trading strategies and use machine learning for finance and trading.

And, so have written this review-driven guide to suggest the best courses for each subject within Machine learning for finance, and for this article, we’ve put some effort into trying to identify high-quality courses from notable educators.

Please, refer to the Closing Note section at the tail end of this piece, where we usually add adjunctive resources.

Now, without further ado, let’s get started.

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11 Best Machine Learning for Finance Courses (Reinforcement Learning and Algorithmic Trading)

These courses will equip you to become highly prepared for the Machine Learning and Reinforcement Learning roles in Finance industry.


Fundamentals of Machine Learning in Finance

This interactive course designed by NYU aims at helping learners to solve practical Machine Learning problems.

In this 4-module course, you will understand the fundamentals of Machine Learning in Finance and dig a little deeper to understand supervised, unsupervised, and reinforcement learning, and end up in a project on using unsupervised learning for implementing a simple portfolio trading strategy.

What you will learn?

Fundamentals of Machine Learning in Finance
Image by Coursera

Is it right for you?

This course is suitable for learners with experience in python programming and the knowledge of college-level mathematics.

By the end, you will be highly prepared to use Supervised and Unsupervised Learning, Reinforcement Learning, and Python packages to design, test, and implement ML algorithms in Finance.


Artificial Intelligence for Trading

Udacity offers this nano degree program in partnership with WorldQuant to help learners gain a strong familiarity with quantitative trading.

This program aims to equip students with employable skills in quantitative analysis, data processing, trading signal generation, and portfolio management.

What you will learn?

  • Basics of Quantitative Analysis and Quantitative Trading.
  • Advanced Quantitative Trading
  • Factor Investing and Alpha Research
  • Sentiment Analysis with Natural Language Processing
  • Advanced Natural Language Processing with Deep Learning
  • Combining Multiple Signals
  • Simulating Trades with Historical Data
Artificial Intelligence for Trading
Image by Udacity

Is it right for you?

This program is suitable for experienced Python programmers with a background in mathematics.

Throughout the series of lectures and projects, you’ll use Python to work with the stock data for developing trading strategies and constructing the multi-factor models with optimization.

By the end, you’ll have gained sound skills AI skills to perform advanced quantitative analysis, including data processing, trading signals.



Machine Learning for Finance in Python

This interactive course offered by DataCamp is taught by Nathan George, who is an Assistant Professor of Data Science at Regis University.

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.

What you will learn?

Machine Learning for Financ
Image by DataCamp

Is it right for you?

This intermediate-level course is suitable for Python programmers, with sound knowledge of Supervised Learning with scikit-learn.

By the end, you’ll be equipped with advanced familiarity to work and prepare features for linear models, xgboost models, and neural network models.


Introduction to Trading, Machine Learning & GCP

This interactive course offered by Google Cloud and the New York Institute of Finance aims to equip finance professionals and machine learning professionals who seek to upgrade their skills for trading strategies.

This course is suitable for understanding the fundamental concepts of trading and Cloud Machine Learning with the Google Cloud Platform.

What you will learn?

  • Introduction to Machine Learning for Finance
  • Supervised/ Unsupervised and Regression/ Classification
  • Basic quantitative trading strategies
  • Time Series and ARIMA Modeling
  • Introduction to Neural Networks and Deep Learning
  • Using Google Cloud Platform to build basic machine learning models.
Machine Learning Trading
Image by Coursera

Is it right for you?

This course assumes experience in Python programming and familiarity with Scikit-Learn, StatsModels, and Pandas.

You must also have a solid background in statistics and knowledge of financial markets.

By the end, you will become highly prepared and skilled in Machine Learning for Finance, Trading and Investment.


Professional Certificate in Machine Learning and Finance

This certification program offered by NYU comprises 2 skill-building courses that help learners build a deeper understanding of machine learning in finance, including but not limited to the applications of supervised learning (regression and classification) and unsupervised learning.

This certification program equips learners with sound skills to build machine learning models to solve practical problems in finance.

What you will learn?

  • Machine Learning for Financial Engineering
  • Utilizing classical machine learning models
  • Deep Learning applications in Financial Engineering
  • Neural Networks in Financial Engineering
Machine Learning Finance
Image by edX

Is it right for you?

This course is suitable for intermediate-level programmers with sound skills in mathematics.

Through the guided series of lectures and exercises, you’ll inculcate employable skills to use various applications of Machine Learning, Deep Learning and AI for Financial Engineering.

Upon successful completion, you’ll have acquired sound Machine Learning skills for Finance.



Machine Learning and Reinforcement Learning in Finance Specialization

This specialization by NYU helps the learners extend their expertise in algorithms and tools needed to predict financial markets.

In this four-course specialization, you will primarily focus on applications of Machine Learning to various practical problems in Finance.

What you will learn?

  • Fundamentals of Machine Learning
  • Reinforcement learning in Finance.
  • Supervised Machine Learning methods
  • Learn the key concepts of reinforcement Learning in stock trading
  • Learn advanced methods of reinforcement learning in finance
  • Become highly familiar with popular approaches to modeling market frictions
Reinforcement Learning in Finance
Image by Coursera

Is it right for you?

This intermediate-level specialization assumes experience in python programming and a solid background in statistics.

By the end, you will have improved skills in Predictive Modelling, Financial Engineering, Machine Learning, TensorFlow, Reinforcement Learning and much more.


Using Machine Learning in Trading and Finance

The New York Institute of Finance and Google Cloud offers this interactive course to equip learners in Algorithmic Trading.

This is course is perfect to learn Quantitative Trading and TensorFlow to help you deeply understand the Trading Strategy Prediction Model.

What you will learn?

Machine Learning in Trading and Finance
Image by Coursera

Is it right for you?

This intermediate-level course is suitable for learners with experience in Python, familiarity with scikit-learn, Statsmodels and Pandas library and also solid background in Statistics.

By the end, you will gain skills in Algorithmic Trading and Machine Learning for Finance and trading with Python.


Reinforcement Learning for Trading Strategies

This course is offered by The New York Institute of Finance and Google Cloud, and delivered via Coursera.

In this course, you will learn to structure and techniques used in reinforcement learning (RL) strategies for trading.

What you will learn?

  • Understand reinforcement learning for model development
  • Learn the concepts of trading algorithm optimization
  • Devise a trading strategy development
  • Become highly skilled in reinforcement learning trading for algorithm development
Reinforcement Learning for Trading Strategies
Image by Coursera

Is it right for you?

This intermediate-level course is good for experienced Python programmers with skills in Pandas, Scikit-learn and Statsmodel and who also have a solid background in Statistics.

By the end, you will be highly equipped to build trading strategies using reinforcement learning.


Machine Learning for Trading Specialization

This brisk specialization is designed by Google Cloud and The New York Institute of Finance and offered via Coursera.

In this 3-Course specialization, you will hone your craft in quantitative trading strategies.

What you will learn?

  • Fundamental concepts of Trading, Machine Learning and Google Cloud Platform.
  • Learn to design quantitative trading strategies.
  • Basics of Reinforcement Learning
  • Reinforcement Learning for the Trading Strategies
Machine Learning for Trading Specialization
Image by Coursera

Is it right for you?

This intermediate-level specialization is suitable for learners with experience in Python programming and familiarity with skills in machine learning, such as Scikit-Learn, StatsModels, and Pandas.

You must have a college-level knowledge of statistics and a basic understanding of financial markets. Also, experience with SQL will be helpful to get ahead faster.

By the end of the specialization, you will be highly equipped to create quantitative trading strategies you can train, test and implement in live markets.


Professional Certificate in Fintech: The Future of Finance

This certification program offered by The University of Texas comprises 4 interactive skill-building courses that cover a wide range of topics to equip learners with expertise in the intersection of finance and technology.

You’ll learn about the business opportunities and growth areas in the finance industry, and advancements that are radically changing operations for efficient financial services by using the applications of Machine learning, Blockchain, APIs and IoT.

What you will learn?

  • Examine blockchain technology and its applications in Fintech.
  • Learn about the applications of AI and Machine learning in finance.
  • Review the technological advancements of payments, API, open banking, insurance and IoT in finance.
Machine Learning For Finance
Image by edX

Is it right for you?

This certification program is suitable for beginners and does not require a technical background.

You’ll learn everything about the advancements that are radically changing how financial services are being offered across the globe and get an in-depth overview of Blockchain in finance.

By the end, you’ll have sound knowledge about the applications of artificial intelligence and machine learning in the finance industry, from credit scoring models in marketplace lending and crowdfunding to algorithmic trading and Robo-advising.


Investment Management with Python and Machine Learning Specialization

This highest-rated specialization offered by EDHEC Business School comprises 4 high-quality courses to help learners build skills for making sound investment decisions, with an emphasis on foundational theory and underlying concepts with practical applications and implementation.

Through the series of dedicated lab sessions, you’ll learn to use Python for implementing machine learning techniques in investment decisions, including data science.

What you will learn?

  • Portfolio Construction and Analysis with Python
  • Advanced Portfolio Construction and Analysis with Python
  • Python for Machine Learning in Asset Management
  • Python for Machine-Learning in Asset Management with Alternative Data Sets
Machine Learning Finance Python
Image by Coursera

Is it right for you?

This specialization is suitable for intermediate learners who are experienced in Python, however, a background in Machine Learning or Data Science is unnecessary.

By the end of this specialization, you’ll have gained solid Machine Learning skills for Investment Management using Python for performing risk analysis and portfolio optimization and developing advanced data visualizations.


CLOSING NOTES

It’s actually no surprise that we can use convolutional neural networks for time series analysis.

It might seem strange because they are used for image-related tasks however, researchers are using convolutional networks for sequence classification.

And since stock prices are a sequence, we can use them to make predictions.

So, we can use Tensorflow.js library to test out a prediction model for the stock prices.

In short, you'd richly benefit from learning TensorFlow.

And, if you would like to audit a course for free, here’s a recent compilation of the Best TensorFlow courses that can help you upgrade your skills in deep learning.

Before you go, I want to highlight that you must have a sound knowledge of mathematical concepts for machine learning in finance.

And, we’ve already written a slew of posts about pre-requisites for Machine Learning. One Math for Machine Learning and one about Mathematics for Data Science to help you become highly prepared for learning advanced Machine Learning courses.


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