Fundamentals of Machine Learning in Finance from Coursera

The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance.

A learner with some or no previous knowledge of Machine Learning (ML) will get to know main algorithms of Supervised and Unsupervised Learning, and Reinforcement Learning, and will be able to use ML open source Python packages to design, test, and implement ML algorithms in Finance.

Fundamentals of Machine Learning in Finance will provide more at-depth view of supervised, unsupervised, and reinforcement learning, and end up in a project on using unsupervised learning for implementing a simple portfolio trading strategy.

The course is designed for three categories of students:

Practitioners working at financial institutions such as banks, asset management firms or hedge funds

Individuals interested in applications of ML for personal day trading

Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance

Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory and basic calculus is necessary to complete assignments in this course.

What's inside

Syllabus

Fundamentals of Supervised Learning in Finance

Core Concepts of Unsupervised Learning, PCA & Dimensionality Reduction

Data Visualization & Clustering

Sequence Modeling and Reinforcement Learning

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores various aspects of the finance industry, including trading, portfolio analysis, and investment strategies, making it relevant to practitioners in these fields

Covers advanced topics such as time series analysis and natural language processing, suitable for individuals with an intermediate level of understanding

Taught by Igor Halperin, who is recognized for his work in the field of financial machine learning, providing learners with access to expert knowledge

Requires prior experience with Python, linear algebra, probability theory, and calculus, making it suitable for learners with a strong background in mathematics and computer science

Assumes familiarity with the basics of machine learning, which may pose a barrier for beginners looking to start from scratch

Does not offer hands-on labs or interactive materials, which could limit the practical application of the concepts learned

Reviews summary

Ml in finance: well-received but inconsistent

Learners say that the "Fundamentals of Machine Learning in Finance" course offers great explanations, relevant finance examples, and a capstone project focused on unsupervised learning. However, the course has received mixed reviews regarding its programming assignments. While some students describe the assignments as frustrating and poorly designed, others find them interesting. Students also note that the course lacks support from staff and that the discussion forums are not adequately monitored.

The course features a capstone project focused on unsupervised learning.

"The course culminates in a capstone project that leverages unsupervised learning for a trading strategy."

Students find the lectures to be engaging and informative.

"Lectures are great -- good content and concise."

"Good course. but requires lot of patience. Uses lot of unnecessary history, symbols and equations to explain simple concepts."

"The video lectures are quite nice although I miss the promised link to finance at time."

The course is praised for its clear explanations and relevant finance examples.

"Great explanations and great material."

"The lecture is ok but lacks of details and the project is poorly designed without much guidance."

"Lectures assume that students know about Finance."

Students report a lack of support from staff and neglect of the discussion forums.

"Not enough support from the staff."

"The same issues have been highlighet by the students months after months and there is no support from the staff."

"Staff doesn't pay any attention to students' complaints."

Programming assignments are described as frustrating and poorly designed.

"The programming assignments are poorly designed and hard to grasp."

"The instructions are confusing and the final exercise requires a very long calculation that can time out."

"The assignments barely relate to the lectures."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Fundamentals of Machine Learning in Finance with these activities:

Review the foundations of supervised learning

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Supervised learning is a cornerstone of machine learning in finance, so it is critical to have a solid grasp before diving into the course.

Browse courses on Supervised Learning

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Review the basic concepts of supervised learning, such as classification and regression.
Practice implementing simple supervised learning algorithms, such as linear regression and logistic regression.

Follow tutorials on using Python for machine learning

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Python is the primary programming language used in this course. By completing this activity, you will be better prepared to follow along with course materials.

Browse courses on Python

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Find tutorials on using Python for machine learning.
Follow the tutorials and practice writing Python code.

Review the book 'Machine Learning for Finance'

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This book provides a comprehensive overview of machine learning in finance and will help you to understand the fundamental concepts.

View Causal Factor Investing (Elements in... on Amazon

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Read the book and take notes on the key concepts.
Complete the exercises and activities in the book.

Five other activities

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Show all eight activities

Solve practice problems on machine learning

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Solving practice problems will help you to solidify your understanding of the concepts covered in the course and improve your problem-solving skills.

Browse courses on Machine Learning

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Find practice problems on machine learning, such as those found on Kaggle or LeetCode.
Solve the practice problems using the techniques you have learned in the course.

Attend a machine learning workshop

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Attending a machine learning workshop will allow you to learn from experts in the field and network with other professionals.

Browse courses on Machine Learning

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Find a machine learning workshop that is relevant to your interests.
Register for the workshop and attend the sessions.

Create a blog post or article on a machine learning topic

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Creating a blog post or article will help you to synthesize your knowledge of machine learning and finance and improve your communication skills.

Browse courses on Machine Learning

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Choose a machine learning topic that you are interested in.
Research the topic and gather information from credible sources.
Write a blog post or article that explains the topic in a clear and concise way.

Start a project on applying machine learning to a real-world financial problem

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Starting a project will allow you to apply the concepts you have learned in the course to a real-world problem and develop your skills in machine learning.

Browse courses on Machine Learning

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Identify a real-world financial problem that you would like to solve using machine learning.
Gather data and prepare it for analysis.
Build and train a machine learning model to solve the problem.
Evaluate the performance of the model and make improvements as needed.

Create a presentation on your machine learning project

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Creating a presentation will help you to synthesize your knowledge of the project and improve your presentation skills.

Browse courses on Machine Learning

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Gather the materials you need for your presentation, such as slides, handouts, and data.
Organize your presentation into a logical flow.
Practice delivering your presentation.

Career center

Learners who complete Fundamentals of Machine Learning in Finance will develop knowledge and skills that may be useful to these careers:

Financial Analyst

Financial Analysts use ML to build complex models to aid in making critical business decisions about capital, investment, and risk management, among other things. This course, with its emphasis on Supervised and Unsupervised Learning in Finance, and Reinforcement Learning, will help you build a foundation toward success. Practitioners working at financial institutions such as banks, asset management firms, or hedge funds will find this course particularly helpful.

See salaries and explore the career path for Financial Analyst

Quantitative Analyst

Quantitative Analysts use ML to identify trends, build models, and make better-informed trading and investment decisions. This course will serve as an excellent start or continuation to your studies. Familiarity with Supervised and Unsupervised Learning in Finance and Reinforcement Learning will help you thrive in the role of a Quantitative Analyst.

See salaries and explore the career path for Quantitative Analyst

Data Scientist

Data Scientists use ML to solve complex problems, from fraud detection to model building. This course will prove to be an excellent foundation on the path to becoming a Data Scientist. As the course description emphasizes, you will learn how to understand which particular ML approach(es) would be most appropriate for resolving a problem. This skill is crucial to success in the field.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers use ML to build and maintain models. This course can help you build a foundation toward becoming a Machine Learning Engineer. Familiarity with Supervised and Unsupervised Learning in Finance and Reinforcement Learning is in high-demand for Machine Learning Engineers.

See salaries and explore the career path for Machine Learning Engineer

Risk Analyst

Risk Analysts use ML to predict financial risks and identify solutions to prevent or mitigate those risks. Gaining a better understanding of Supervised and Unsupervised Learning, and Reinforcement Learning, as you will in this course, will prove highly valuable in this role.

See salaries and explore the career path for Risk Analyst

Actuary

Actuaries use ML to assess risk and create financial models. This course will help you develop the foundational knowledge required to succeed in this role. The course's focus on Supervised and Unsupervised Learning in Finance and Reinforcement Learning will serve you well.

See salaries and explore the career path for Actuary

Financial Risk Manager

Financial Risk Managers use ML to identify and quantify financial risks. This course may be helpful in your preparation for this role. Understanding the fundamentals of Supervised and Unsupervised Learning, and Reinforcement Learning will prove valuable.

See salaries and explore the career path for Financial Risk Manager

Investment Analyst

Investment Analysts use ML to make better-informed investment decisions. This course can help you build a foundation toward becoming an Investment Analyst. Familiarity with Supervised and Unsupervised Learning and Reinforcement Learning is in high-demand for Investment Analysts.

See salaries and explore the career path for Investment Analyst

Quantitative Researcher

Quantitative Researchers use ML to develop new financial products and strategies. The course on Fundamentals of Machine Learning in Finance can help you to develop the skills needed to be successful as a Quant Researcher.

See salaries and explore the career path for Quantitative Researcher

Financial Planner

Financial Planners use ML to help clients make financial decisions such as retirement planning, investment management, and wealth management. This course may be useful in your preparation for this role. Understanding the fundamentals of Supervised and Unsupervised Learning, and Reinforcement Learning will prove valuable.

See salaries and explore the career path for Financial Planner

Business Analyst

Business Analysts use ML to solve business problems, from optimizing marketing campaigns to improving customer service. This course may be useful in your preparation for this role. Understanding the fundamentals of Supervised and Unsupervised Learning, and Reinforcement Learning will prove valuable.

See salaries and explore the career path for Business Analyst

Software Engineer

Software Engineers use ML to build and maintain software applications. This course may be useful in your preparation for this role. Understanding the fundamentals of Supervised and Unsupervised Learning, and Reinforcement Learning will prove valuable.

See salaries and explore the career path for Software Engineer

Data Analyst

Data Analysts use ML to analyze data and create visualizations. This course may be useful in your preparation for this role. Understanding the fundamentals of Supervised and Unsupervised Learning, and Reinforcement Learning will prove valuable.

See salaries and explore the career path for Data Analyst

Statistician

Statisticians use ML to analyze data and draw conclusions. This course may be useful in your preparation for this role. Understanding the fundamentals of Supervised and Unsupervised Learning, and Reinforcement Learning will prove valuable.

See salaries and explore the career path for Statistician

Economist

Economists use ML to forecast economic trends. This course may be useful in your preparation for this role. Understanding the elements of Supervised and Unsupervised Learning, and Reinforcement Learning will prove valuable.

See salaries and explore the career path for Economist