Fundamentals of Machine Learning in Finance
This course provides an introduction to the fundamentals of machine learning in finance, equipping students with the skills to solve practical ML-amenable problems encountered in the financial industry. ▼
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Course Feature
Cost:
Free
Provider:
Coursera
Certificate:
No Information
Language:
English
Start Date:
Self Paced
Course Overview
❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.
Updated in [March 06th, 2023]
This course provides an introduction to the fundamentals of Machine Learning in Finance. It covers topics such as Support Vector Machines (SVM), the Kernel Trick, and example applications of SVM for predicting credit spreads. Additionally, it covers tree methods such as CART Trees, Random Forests, and Boosting. By the end of the course, students will have a better understanding of the fundamentals of Machine Learning in Finance and how to apply them in practice.
[Applications]
The application of this course can be seen in the financial industry, where machine learning can be used to predict credit spreads, identify trends in stock prices, and detect fraudulent activities. Additionally, machine learning can be used to develop automated trading systems, optimize portfolio management, and improve risk management. Furthermore, machine learning can be used to develop algorithms for algorithmic trading, and to develop models for predicting market movements. Finally, machine learning can be used to develop models for predicting customer behavior and to develop models for predicting customer sentiment.
[Career Paths]
1. Financial Data Scientist: Financial data scientists use machine learning algorithms to analyze large datasets and develop predictive models to help financial institutions make better decisions. They are responsible for developing and deploying machine learning models to identify patterns in financial data, such as stock prices, customer behavior, and market trends. The development of AI and machine learning technologies has created a demand for financial data scientists, and the trend is expected to continue in the coming years.
2. Quantitative Analyst: Quantitative analysts use mathematical and statistical models to analyze financial data and develop strategies for trading and investing. They use machine learning algorithms to identify patterns in financial data and develop predictive models to help financial institutions make better decisions. The demand for quantitative analysts is expected to grow in the coming years as more financial institutions adopt machine learning technologies.
3. Risk Manager: Risk managers use machine learning algorithms to identify and manage risks in financial markets. They use predictive models to identify potential risks and develop strategies to mitigate them. The demand for risk managers is expected to grow in the coming years as more financial institutions adopt machine learning technologies.
4. Investment Banker: Investment bankers use machine learning algorithms to analyze financial data and develop strategies for trading and investing. They use predictive models to identify potential opportunities and develop strategies to capitalize on them. The demand for investment bankers is expected to grow in the coming years as more financial institutions adopt machine learning technologies.
[Education Paths]
1. Bachelor of Science in Financial Engineering: This degree program focuses on the application of mathematical and computational methods to the analysis of financial markets and instruments. It combines the study of finance, economics, mathematics, and computer science to develop quantitative models and strategies for financial decision-making. This degree is becoming increasingly popular as the demand for quantitative skills in the financial industry continues to grow.
2. Master of Science in Financial Mathematics: This degree program focuses on the application of mathematical and statistical methods to the analysis of financial markets and instruments. It combines the study of finance, economics, mathematics, and computer science to develop quantitative models and strategies for financial decision-making. This degree is becoming increasingly popular as the demand for quantitative skills in the financial industry continues to grow.
3. Master of Science in Machine Learning: This degree program focuses on the application of machine learning algorithms and techniques to the analysis of financial markets and instruments. It combines the study of finance, economics, mathematics, and computer science to develop quantitative models and strategies for financial decision-making. This degree is becoming increasingly popular as the demand for quantitative skills in the financial industry continues to grow.
4. Master of Science in Artificial Intelligence: This degree program focuses on the application of artificial intelligence algorithms and techniques to the analysis of financial markets and instruments. It combines the study of finance, economics, mathematics, and computer science to develop quantitative models and strategies for financial decision-making. This degree is becoming increasingly popular as the demand for quantitative skills in the financial industry continues to grow.
Course Syllabus
Core Concepts of UL
PCA for Stock Returns
Dimension Reduction with PCA
Dimension Reduction with tSNE
Dimension Reduction with Autoencoders
Pros & Cons
Repeated material from previous course.
Community of students to figure things out together.
Good content.
Poor direction on how to pursue learning.
Poor feedback on code performance.
Poorly designed assignments.
Course Provider
Provider Coursera's Stats at AZClass
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