Learn Machine Learning for Finance from the World’s top Educators

Machine Learning for finance offers enormous potential and it also involves techniques that can be exceedingly challenging to understand without an effective teacher.

I know the options out there, and what skills are needed for learners to effectively understand quantitative trading strategies and using machine learning for finance and trading.

So, I developed this review-driven guide to suggest the best courses for each subject within Machine learning for finance, and for this article, I’ve put some efforts in trying to identify every best course offered by the notable educators.

Also, please refer to the Closing Notes section at the tail end of this piece, where I usually add adjunctive resources, mostly helpful for overcoming learning blocks.

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

7 Best Courses to learn Machine Learning for Finance

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

— Machine Learning for Finance

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 model, decision trees, random forests, and neural networks to predict the future price of stocks.

What you will learn?

Machine Learning for Finance with Python - 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 New York Institute of Finance, aims to equip finance professionals, and machine learning professionals who seek upgrade their skills for trading strategies.

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

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.

— Machine Learning and Reinforcement Learning in Finance Specialization

This specialization by NYU is designed to help the learners extend their expertise of algorithms and tools needed to predict financial markets.

In this four-course specialization, you will primarily focus on applications of the 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

Is it right for you?

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

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

— Machine Learning for Trading Specialization

This brisk specialization is designed by Google Cloud and 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

Is it right for you?

This intermediate-level specialization is suitable for learners with good experience of 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 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 that you can train, test and implement in live markets.

— Fundamentals of Machine Learning in Finance

This interactive course designed by NYU aims at helping learners to be able 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? 

Is it right for you?

This course is suitable for learner who are experienced in python programming and college-level knowledge of mathematics.

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

— Using Machine Learning in Trading and Finance

This interactive course is offered by New York Institute of Finance and Google Cloud 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?

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 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

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.

Closing Notes

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

It might seem strange b’coz 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, It would richly help you to learn Tensorflow.

And, if you would like to audit a course for free, here’s my recent compilation on best tensorflow courses that can help you to upgrade your skills in deep learning.

Before you go, I want to highlight that you must to have a sound knowledge of mathematical concepts for machine learning in finance. So, I’ve another practical read for you about Mathematics for Data Science classes that can help you to become highly prepared for learning advanced Machine Learning courses.

Thanks for making it to the end ;)

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