NYU - Guided Tour of Machine Learning in Finance
- Offered byCoursera
Guided Tour of Machine Learning in Finance at Coursera Overview
Duration | 24 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Guided Tour of Machine Learning in Finance at Coursera Highlights
- 50% started a new career after completing these courses.
- 47% got a tangible career benefit from this course.
- Earn a shareable certificate upon completion.
Guided Tour of Machine Learning in Finance at Coursera Course details
- 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.
Guided Tour of Machine Learning in Finance at Coursera Curriculum
Artificial Intelligence & Machine Learning
Welcome Note
Specialization Objectives
Specialization Prerequisites
Artificial Intelligence and Machine Learning, Part I
Artificial Intelligence and Machine Learning, Part II
Machine Learning as a Foundation of Artificial Intelligence, Part I
Machine Learning as a Foundation of Artificial Intelligence, Part II
Machine Learning as a Foundation of Artificial Intelligence, Part III
Machine Learning in Finance vs Machine Learning in Tech, Part I
Machine Learning in Finance vs Machine Learning in Tech, Part II
Machine Learning in Finance vs Machine Learning in Tech, Part III
The Business of Artificial Intelligence
How AI and Automation Will Shape Finance in the Future
A. Geron, ?Hands-On Machine Learning with Scikit-Learn and TensorFlow?, Chapter 1
Module 1 Quiz
Mathematical Foundations of Machine Learning
Generalization and a Bias-Variance Tradeoff
The No Free Lunch Theorem
Overfitting and Model Capacity
Linear Regression
Regularization, Validation Set, and Hyper-parameters
Overview of the Supervised Machine Learning in Finance
I. Goodfellow, Y. Bengio, A. Courville, ?Deep Learning?, Chapters 4.5, 5.1, 5.2, 5.3, 5.4
Leo Breiman, ?Statistical Modeling: The Two Cultures?
Jupyter Notebook FAQ
Module 2 Quiz
Introduction to Supervised Learning
DataFlow and TensorFlow
A First Demo of TensorFlow
Linear Regression in TensorFlow
Neural Networks
Gradient Descent Optimization
Gradient Descent for Neural Networks
Stochastic Gradient Descent
A.Geron, ?Hands-On ML?, Chapter 9, Chapter 4 (Gradient Descent)
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.
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
Jupyter Notebook FAQ
Module 3 Quiz
Supervised Learning in Finance
Regression and Equity Analysis
Fundamental Analysis
Machine Learning as Model Estimation
Maximum Likelihood Estimation
Probabilistic Classification Models
Logistic Regression for Modeling Bank Failures, Part I
Logistic Regression for Modeling Bank Failures, Part II
Logistic Regression for Modeling Bank Failures, Part III
Supervised Learning: Conclusion
C. Bishop, ?Pattern Recognition and Machine Learning?, Chapters 4.1, 4.2, 4.3
A. Geron, ?Hands-On ML?, Chapters 3, Chapter 4 (Logistic Regression)
Jupyter Notebook FAQ
Jupyter Notebook FAQ
Module 4 Quiz
Guided Tour of Machine Learning in Finance at Coursera Admission Process
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