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Igor Halperin
This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance.
Read more
This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance.
Read more
This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance.
The goal of Guided Tour of Machine Learning in Finance is to get a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to.
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/9t7689/guided
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What's inside
Syllabus
Artificial Intelligence & Machine Learning
Mathematical Foundations of Machine Learning
Introduction to Supervised Learning
Read more
Syllabus
Artificial Intelligence & Machine Learning
Mathematical Foundations of Machine Learning
Introduction to Supervised Learning
Read more
Supervised Learning in Finance
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Well-suited for individuals seeking applications of machine learning in the financial sector, including practitioners, personal day traders, and students
Provides a broad overview of machine learning concepts and their relevance to finance, making it accessible to learners with diverse backgrounds
Emphasizes practical applications of supervised machine learning methods, enhancing its relevance to real-world financial scenarios
Leverages a hands-on capstone project, allowing learners to apply their knowledge and gain practical experience in predicting bank closures
Serves as a valuable introduction to the Machine Learning and Reinforcement Learning in Finance specialization, providing a foundation for further exploration
Requires prerequisite knowledge in Python, linear algebra, basic probability theory, and basic calculus, which may limit accessibility for some learners
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Reviews summary
Practical introduction to ml in finance
Learners say this course provides a practical, well structured introduction to using machine learning for financial applications. The engaging lectures are informative and assume familiarity with statistics and linear algebra. However, some learners found the course difficult and the assignments unclear without a strong background in Python and TensorFlow. Despite these challenges, many learners found the course worthwhile and enjoyable. This course is best suited for experienced ML professionals seeking a general introduction to ML applications in finance, or those with prior experience in the field.
Lectures are clear and informative, with a focus on financial applications.
"The lectures and the concept for this course were very good."
"The problem was that it wasn't "guided" in any sense."
"There was a lot of time focusing on math concepts, but the way to apply those concepts in the code were glossed over or at times not even mentioned."
"The labs often asked you to do things that weren't covered at all in the lessons, forcing you to basically learn the coding through Googling."
"The forums weren't being monitored either, so if you felt like you were most of the way there but not getting the correct answer, there was no way to get a little guidance."
Offers a practical approach, emphasizing the applications of machine learning in finance.
"The "Guided Tour of Machine Learning in Finance" course introduces machine learning concepts emphasizing their applications in finance."
"It guides participants through the foundational concepts of machine learning, mainly supervised learning."
"It includes four modules, each offering theoretical knowledge and practical experience."
"It is open to a diverse audience, including financial professionals and students from various disciplines."
Assumes familiarity with statistics, linear algebra, and Python libraries.
"Prof. Halperin certainly does have a great deal of practical experience on this subject, having worked in the financial sector for several decades."
"As such, the lecture videos are succinct and informative."
"(I will issue the caveat that in order to make the most of this course, one should be already be comfortable with linear algebra, statistics, some calculus and the python libraries mentioned in the prerequisites.)"
"The readings are mostly relevant, at times tangential and in some cases completely off-topic (albeit still somewhat interesting)."
"What perhaps makes this course frustrating, as some have already noted, is that some of the code is outdated."
Assignments can be unclear and difficult to complete without strong programming skills.
"The lectures are actually very good, but I think it would help tremendously if you can make the slides and sample Jupiter notebooks used in lecture available to us."
"It takes us a lot of time to recreate the notebooks just to play around with them."
"Great contents."
"Excellent topic."
"But poor explanation especially in coding assignment."
Uses outdated versions of TensorFlow, which can be inconvenient.
"The lectures that are present are useful."
"However, I feel like the course is broken with some of the videos missing, as the lecturer references topics/items from supposedly previous videos that were never mentioned (this occurs specifically in Week 4, where the section "Prediction of Earning per Share (EPS) with Scikit-learn and TensorFlow" only contains basic videos with an introduction to types of equity analysis and what fundamental analysis is, but there are no videos with actual Scikit-learn/Tensorflow examples)."
"The weekly quizzes are trivial - they just recycle the knowledge check questions from within the video, and as standalone questions often don't really make any sense."
"The programming assignments are very sparse on instructions or information of what is expected."
"So while students do get some hands-on experience implementing some things in sklearn and TensorFlow, for the majority of the time they're 'flying blind'."
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 Guided Tour of Machine Learning in Finance with these
activities:
Practice solving problems on supervised learning
Show steps
Regular practice will help you develop a strong foundation in supervised learning algorithms and techniques.
Browse courses on
Supervised Learning
Show steps
-
Solve practice problems from online resources or textbooks
-
Participate in online coding challenges
Review 'Machine Learning for Finance' by Marcos López de Prado
Show steps
Reading this reference text will strengthen your understanding of the mathematical foundations of machine learning, as well as its applications in finance.
View
Causal Factor Investing (Elements in...
on Amazon
Show steps
-
Read chapters 1-5
-
Complete practice problems at the end of each chapter
-
Summarize key concepts in your own words
Follow tutorials on machine learning applications in finance
Show steps
These tutorials will provide concrete examples of how machine learning is being used to solve real-world financial problems.
Browse courses on
Machine Learning
Show steps
-
Search for tutorials on platforms like Coursera, Udemy, or YouTube
-
Follow the tutorials and complete the exercises
Six other activities
Expand to see all activities and additional details
Show all nine activities
Curate a collection of resources on machine learning for finance
Show steps
Creating a compilation of resources will help you stay up-to-date on the latest developments in the field and deepen your understanding of the topic.
Browse courses on
Machine Learning
Show steps
-
Use a tool like Google Scholar, Mendeley, or Zotero to gather resources
-
Organize the resources into categories or topics
-
Share your compilation with other students or colleagues
Attend a workshop on machine learning for finance
Show steps
Attending a workshop will provide you with an opportunity to learn from experts and network with other professionals in the field.
Browse courses on
Machine Learning
Show steps
-
Research workshops in your area or online
-
Register for a workshop that aligns with your interests
-
Attend the workshop and actively participate in discussions
Contribute to an open-source project on machine learning for finance
Show steps
Contributing to an open-source project is a great way to learn from others and give back to the community.
Browse courses on
Machine Learning
Show steps
-
Find an open-source project on GitHub or another platform
-
Identify an issue or feature that you can contribute to
-
Submit a pull request with your changes
Build a machine learning model to predict bank closures
Show steps
This project will provide hands-on experience with supervised learning algorithms and will help you apply machine learning techniques to a real-world financial problem.
Browse courses on
Supervised Learning
Show steps
-
Gather and preprocess data on bank closures
-
Select and train a machine learning model
-
Evaluate the performance of your model
-
Write a report summarizing your findings
Review 'Deep Learning for Finance' by Jason Brownlee
Show steps
This book will give you an overview of deep learning and its applications in finance. It will help you understand the potential and limitations of deep learning models.
Show steps
-
Read chapters 1-4
-
Complete practice problems at the end of each chapter
-
Summarize key concepts in your own words
Develop a ML app to predict stock prices
Show steps
This project will provide hands-on experience with developing and deploying a machine learning application, and gain valuable experience in the financial industry.
Browse courses on
Machine Learning
Show steps
-
Gather and preprocess historical stock data
-
Train a machine learning model to predict stock prices
-
Deploy your model as a web application
-
Monitor and evaluate the performance of your application
Practice solving problems on supervised learning
Show steps
Regular practice will help you develop a strong foundation in supervised learning algorithms and techniques.
Browse courses on
Supervised Learning
Show steps
Show steps
Show steps
- Solve practice problems from online resources or textbooks
- Participate in online coding challenges
Review 'Machine Learning for Finance' by Marcos López de Prado
Show steps
Reading this reference text will strengthen your understanding of the mathematical foundations of machine learning, as well as its applications in finance.
View
Causal Factor Investing (Elements in...
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-5
- Complete practice problems at the end of each chapter
- Summarize key concepts in your own words
Follow tutorials on machine learning applications in finance
Show steps
These tutorials will provide concrete examples of how machine learning is being used to solve real-world financial problems.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Search for tutorials on platforms like Coursera, Udemy, or YouTube
- Follow the tutorials and complete the exercises
Six other activities
Expand to see all activities and additional details
Show all nine activities
Curate a collection of resources on machine learning for finance
Show steps
Creating a compilation of resources will help you stay up-to-date on the latest developments in the field and deepen your understanding of the topic.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Use a tool like Google Scholar, Mendeley, or Zotero to gather resources
- Organize the resources into categories or topics
- Share your compilation with other students or colleagues
Attend a workshop on machine learning for finance
Show steps
Attending a workshop will provide you with an opportunity to learn from experts and network with other professionals in the field.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Research workshops in your area or online
- Register for a workshop that aligns with your interests
- Attend the workshop and actively participate in discussions
Contribute to an open-source project on machine learning for finance
Show steps
Contributing to an open-source project is a great way to learn from others and give back to the community.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find an open-source project on GitHub or another platform
- Identify an issue or feature that you can contribute to
- Submit a pull request with your changes
Build a machine learning model to predict bank closures
Show steps
This project will provide hands-on experience with supervised learning algorithms and will help you apply machine learning techniques to a real-world financial problem.
Browse courses on
Supervised Learning
Show steps
Show steps
Show steps
- Gather and preprocess data on bank closures
- Select and train a machine learning model
- Evaluate the performance of your model
- Write a report summarizing your findings
Review 'Deep Learning for Finance' by Jason Brownlee
Show steps
This book will give you an overview of deep learning and its applications in finance. It will help you understand the potential and limitations of deep learning models.
Show steps
Show steps
Show steps
- Read chapters 1-4
- Complete practice problems at the end of each chapter
- Summarize key concepts in your own words
Develop a ML app to predict stock prices
Show steps
This project will provide hands-on experience with developing and deploying a machine learning application, and gain valuable experience in the financial industry.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Gather and preprocess historical stock data
- Train a machine learning model to predict stock prices
- Deploy your model as a web application
- Monitor and evaluate the performance of your application
Career center
Learners who complete Guided Tour of Machine Learning in Finance will develop knowledge and skills
that may be useful to these careers:
Financial Analyst
Financial Analyst
Financial Analysts use their knowledge of finance and statistics to make recommendations on investments. They may work for banks, investment firms, or other financial institutions. This course can provide you with the necessary skills to become a Financial Analyst. You will learn about different types of financial instruments, how to analyze financial data, and how to make sound investment decisions. The course also covers topics such as portfolio management, risk management, and financial modeling.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts (Quants) use mathematical and statistical models to analyze financial data and make investment decisions. They may work for hedge funds, investment banks, or other financial institutions. This course can provide you with the necessary skills to become a Quant. You will learn about different types of financial instruments, how to analyze financial data, and how to develop and implement financial models.
Risk Manager
Risk Manager
Risk Managers are responsible for assessing and managing financial risks. They may work for banks, investment firms, or other financial institutions. This course can help you build a foundation for a career in Risk Management. You will learn about different types of financial risks, how to measure and manage them, and how to develop and implement risk management policies.
Data Scientist
Data Scientist
Data Scientists use their knowledge of statistics, machine learning, and other data analysis techniques to solve business problems. They may work for a variety of companies, including financial institutions, technology companies, and healthcare companies. This course can provide you with the necessary skills to become a Data Scientist. You will learn about different types of data analysis techniques, how to use machine learning algorithms, and how to interpret and communicate data analysis results.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. They may work for a variety of companies, including financial institutions, technology companies, and healthcare companies. This course can provide you with the necessary skills to become a Machine Learning Engineer. You will learn about different types of machine learning algorithms, how to design and develop machine learning models, and how to deploy machine learning models to production.
Financial Planner
Financial Planner
Financial Planners help individuals and families plan for their financial future. They may work for banks, investment firms, or other financial institutions. This course can provide you with the necessary skills to become a Financial Planner. You will learn about different types of financial products and services, how to develop financial plans, and how to advise clients on financial matters.
Investment Banker
Investment Banker
Investment Bankers help companies raise capital and advise them on mergers and acquisitions. They may work for investment banks or other financial institutions. This course can provide you with the necessary skills to become an Investment Banker. You will learn about different types of financial instruments, how to analyze financial data, and how to develop and execute financial transactions.
Portfolio Manager
Portfolio Manager
Portfolio Managers manage investment portfolios for individuals and families. They may work for banks, investment firms, or other financial institutions. This course can provide you with the necessary skills to become a Portfolio Manager. You will learn about different types of financial instruments, how to analyze financial data, and how to develop and manage investment portfolios.
Hedge Fund Manager
Hedge Fund Manager
Hedge Fund Managers manage investment portfolios for wealthy individuals and institutions. They may work for hedge funds or other financial institutions. This course can provide you with the necessary skills to become a Hedge Fund Manager. You will learn about different types of financial instruments, how to analyze financial data, and how to develop and manage investment portfolios.
Private Equity Investor
Private Equity Investor
Private Equity Investors invest in private companies and help them grow. They may work for private equity firms or other financial institutions. This course can provide you with the necessary skills to become a Private Equity Investor. You will learn about different types of private equity investments, how to analyze companies, and how to structure and negotiate private equity deals.
Venture Capitalist
Venture Capitalist
Venture Capitalists invest in early-stage companies and help them grow. They may work for venture capital firms or other financial institutions. This course can provide you with the necessary skills to become a Venture Capitalist. You will learn about different types of venture capital investments, how to analyze companies, and how to structure and negotiate venture capital deals.
Management Consultant
Management Consultant
Management Consultants help businesses solve problems and improve their performance. They may work for consulting firms or other organizations. This course can help you develop the skills you need to become a Management Consultant. You will learn about different business disciplines, how to analyze business problems, and how to develop and implement solutions.
Business Analyst
Business Analyst
Business Analysts analyze business problems and develop solutions. They may work for a variety of companies, including financial institutions, technology companies, and healthcare companies. This course can provide you with the necessary skills to become a Business Analyst. You will learn about different business disciplines, how to analyze business problems, and how to develop and implement solutions.
Product Manager
Product Manager
Product Managers are responsible for developing and launching new products. They may work for a variety of companies, including technology companies, consumer goods companies, and healthcare companies. This course can help you develop the skills you need to become a Product Manager. You will learn about different product development processes, how to conduct market research, and how to develop and launch new products.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They may work for a variety of companies, including technology companies, financial institutions, and healthcare companies. This course can provide you with the necessary skills to become a Software Engineer. You will learn about different software development methodologies, how to design and develop software systems, and how to test and deploy software systems.
For more career information including salaries, visit:
OpenCourser.com/course/9t7689/guided
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Guided Tour of Machine Learning in Finance.
Provides a practical guide to using machine learning techniques in finance. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and model evaluation. It valuable resource for both practitioners and students who want to learn how to apply machine learning in finance.
Provides a comprehensive overview of artificial intelligence techniques used in finance, including machine learning, natural language processing, and computer vision. It valuable resource for both practitioners and students who want to learn about the application of artificial intelligence in finance.
Save
Provides a comprehensive overview of algorithmic trading techniques, including trading strategies, risk management, and performance analysis. It valuable resource for both practitioners and students who want to learn about the application of algorithmic trading in finance.
Provides a comprehensive overview of risk management techniques used in finance, including market risk, credit risk, and operational risk. It valuable resource for both practitioners and students who want to learn about the application of risk management in finance.
Provides a comprehensive overview of financial econometrics techniques, including time series analysis, regression analysis, and forecasting. It valuable resource for both practitioners and students who want to learn about the application of financial econometrics in finance.
Provides a comprehensive overview of reinforcement learning techniques used in asset management. It valuable resource for both practitioners and students who want to learn about the application of reinforcement learning in asset management.
Provides a comprehensive overview of artificial intelligence techniques used in financial trading. It valuable resource for both practitioners and students who want to learn about the application of artificial intelligence in financial trading.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/9t7689/guided
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Key Concepts Machine Learning
Level
Intermediate
Via
Coursera
Institution
New York University
Instructor
Igor Halperin
Language
English
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آذربایجان دیلی
This course belongs to a
collection
called
Machine Learning and Reinforcement Learning in Finance. It's comprised of four courses:
- Guided Tour of Machine Learning in Finance (viewing)
- Fundamentals of Machine Learning in Finance
- Reinforcement Learning in Finance
- Overview of Advanced Methods of Reinforcement Learning in Finance
Good to know
Well-suited for individuals seeking applications of machine learning in the financial sector, including practitioners, personal day traders, and students
Provides a broad overview of machine learning concepts and their relevance to finance, making it accessible to learners with diverse backgrounds
Emphasizes practical applications of supervised machine learning methods, enhancing its relevance to real-world financial scenarios
Leverages a hands-on capstone project, allowing learners to apply their knowledge and gain practical experience in predicting bank closures
Serves as a valuable introduction to the Machine Learning and Reinforcement Learning in Finance specialization, providing a foundation for further exploration
Requires prerequisite knowledge in Python, linear algebra, basic probability theory, and basic calculus, which may limit accessibility for some learners
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