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⇱ Guided Tour of Machine Learning in Finance | Coursera

Guided Tour of Machine Learning in Finance

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Guided Tour of Machine Learning in Finance

39,285 already enrolled

Included with

Gain insight into a topic and learn the fundamentals.
3.8

682 reviews

Intermediate level
Some related experience required
Flexible schedule
2 weeks at 10 hours a week
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
3.8

682 reviews

Intermediate level
Some related experience required
Flexible schedule
2 weeks at 10 hours a week
Learn at your own pace

Build your subject-matter expertise

This course is part of the Machine Learning and Reinforcement Learning in Finance Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in this course

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.

What's included

11 videos3 readings1 assignment

11 videos•Total 75 minutes
  • Welcome Note•5 minutes
  • Specialization Objectives•8 minutes
  • Specialization Prerequisites•7 minutes
  • Artificial Intelligence and Machine Learning, Part I•6 minutes
  • Artificial Intelligence and Machine Learning, Part II•7 minutes
  • Machine Learning as a Foundation of Artificial Intelligence, Part I•6 minutes
  • Machine Learning as a Foundation of Artificial Intelligence, Part II•7 minutes
  • Machine Learning as a Foundation of Artificial Intelligence, Part III•8 minutes
  • Machine Learning in Finance vs Machine Learning in Tech, Part I•7 minutes
  • Machine Learning in Finance vs Machine Learning in Tech, Part II•6 minutes
  • Machine Learning in Finance vs Machine Learning in Tech, Part III•8 minutes
3 readings•Total 90 minutes
  • The Business of Artificial Intelligence•30 minutes
  • How AI and Automation Will Shape Finance in the Future•30 minutes
  • A. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapter 1•30 minutes
1 assignment•Total 30 minutes
  • Module 1 Quiz•30 minutes

What's included

6 videos3 readings1 assignment1 programming assignment1 ungraded lab

6 videos•Total 45 minutes
  • Generalization and a Bias-Variance Tradeoff•7 minutes
  • The No Free Lunch Theorem•8 minutes
  • Overfitting and Model Capacity•8 minutes
  • Linear Regression•8 minutes
  • Regularization, Validation Set, and Hyper-parameters•11 minutes
  • Overview of the Supervised Machine Learning in Finance•4 minutes
3 readings•Total 130 minutes
  • I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, Chapters 4.5, 5.1, 5.2, 5.3, 5.4•60 minutes
  • Leo Breiman, “Statistical Modeling: The Two Cultures”•60 minutes
  • Jupyter Notebook FAQ•10 minutes
1 assignment•Total 15 minutes
  • Module 2 Quiz•15 minutes
1 programming assignment•Total 90 minutes
  • Euclidean Distance Calculation•90 minutes
1 ungraded lab•Total 60 minutes
  • Euclidean Distance Calculation•60 minutes

What's included

7 videos4 readings1 assignment1 programming assignment1 ungraded lab

7 videos•Total 75 minutes
  • DataFlow and TensorFlow•11 minutes
  • A First Demo of TensorFlow•11 minutes
  • Linear Regression in TensorFlow•11 minutes
  • Neural Networks•11 minutes
  • Gradient Descent Optimization•11 minutes
  • Gradient Descent for Neural Networks•12 minutes
  • Stochastic Gradient Descent•9 minutes
4 readings•Total 100 minutes
  • A.Geron, “Hands-On ML”, Chapter 9, Chapter 4 (Gradient Descent)•60 minutes
  • E. Fama and K. French, “Size and Book-to-Market Factors in Earnings and Returns”, Journal of Finance, vol. 50, no. 1 (1995), pp. 131-155.•15 minutes
  • J. Piotroski, “Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers”, Journal of Accounting Research, Vol. 38, Supplement: Studies on Accounting Information and the Economics of the Firm (2000), pp. 1-41•15 minutes
  • Jupyter Notebook FAQ•10 minutes
1 assignment•Total 15 minutes
  • Module 3 Quiz•15 minutes
1 programming assignment•Total 90 minutes
  • Linear Regression•90 minutes
1 ungraded lab•Total 60 minutes
  • Linear Regression•60 minutes

What's included

9 videos4 readings1 assignment2 programming assignments2 ungraded labs

9 videos•Total 66 minutes
  • Regression and Equity Analysis•8 minutes
  • Fundamental Analysis•8 minutes
  • Machine Learning as Model Estimation•8 minutes
  • Maximum Likelihood Estimation•11 minutes
  • Probabilistic Classification Models•7 minutes
  • Logistic Regression for Modeling Bank Failures, Part I•9 minutes
  • Logistic Regression for Modeling Bank Failures, Part II•6 minutes
  • Logistic Regression for Modeling Bank Failures, Part III•8 minutes
  • Supervised Learning: Conclusion•2 minutes
4 readings•Total 140 minutes
  • C. Bishop, “Pattern Recognition and Machine Learning”, Chapters 4.1, 4.2, 4.3•60 minutes
  • A. Geron, “Hands-On ML”, Chapters 3, Chapter 4 (Logistic Regression)•60 minutes
  • Jupyter Notebook FAQ•10 minutes
  • Jupyter Notebook FAQ•10 minutes
1 assignment•Total 21 minutes
  • Module 4 Quiz•21 minutes
2 programming assignments•Total 180 minutes
  • Tobit Regression•90 minutes
  • Course Project•90 minutes
2 ungraded labs•Total 180 minutes
  • Tobit Regression•60 minutes
  • Course Project•120 minutes

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Instructor

Instructor ratings
3.9 (78 ratings)
New York University
4 Courses•59,692 learners

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Learner reviews

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Showing 3 of 682

DW
·

Reviewed on Sep 8, 2019

Leans heavily on explaining differences between tech and finance applications of ML, but still great!

KD
·

Reviewed on Aug 23, 2019

Introduction of ML for Financial application with combination of Scikit learn, Statsmodels and Tensorflow with neuralnets made this class very interesting. Learned and Enjoyed lot.

KN
·

Reviewed on Jul 25, 2022

Great course. but requires lot of patience. Uses lot of unnecessary symbols and equations to explain concepts. Overall it is a good overview of the big picture of ML in finance.

Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

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