Fundamentals of Machine Learning in Finance faq

star-rating
3.8
learnersLearners: 12,800
instructor Instructor: / instructor-icon
duration Duration: duration-icon

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.

ADVERTISEMENT

Course Feature Course Overview Pros & Cons Course Provider Discussion and Reviews
Go to class

Course Feature

costCost:

Free

providerProvider:

Coursera

certificateCertificate:

No Information

languageLanguage:

English

start dateStart 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

Pros Cons
  • pros

    Repeated material from previous course.

  • pros

    Community of students to figure things out together.

  • pros

    Good content.

  • cons

    Poor direction on how to pursue learning.

  • cons

    Poor feedback on code performance.

  • cons

    Poorly designed assignments.

Course Provider

Provider Coursera's Stats at AZClass

Discussion and Reviews

0.0   (Based on 0 reviews)

Start your review of Fundamentals of Machine Learning in Finance

faq FAQ for Machine Learning Courses

Q1: How do I contact your customer support team for more information?

If you have questions about the course content or need help, you can contact us through "Contact Us" at the bottom of the page.

Q2: Can I take this course for free?

Yes, this is a free course offered by Coursera, please click the "go to class" button to access more details.

Q3: How many people have enrolled in this course?

So far, a total of 12800 people have participated in this course. The duration of this course is hour(s). Please arrange it according to your own time.

Q4: How Do I Enroll in This Course?

Click the"Go to class" button, then you will arrive at the course detail page.
Watch the video preview to understand the course content.
(Please note that the following steps should be performed on Coursera's official site.)
Find the course description and syllabus for detailed information.
Explore teacher profiles and student reviews.
Add your desired course to your cart.
If you don't have an account yet, sign up while in the cart, and you can start the course immediately.
Once in the cart, select the course you want and click "Enroll."
Coursera may offer a Personal Plan subscription option as well. If the course is part of a subscription, you'll find the option to enroll in the subscription on the course landing page.
If you're looking for additional Machine Learning courses and certifications, our extensive collection at azclass.net will help you.

close

To provide you with the best possible user experience, we use cookies. By clicking 'accept', you consent to the use of cookies in accordance with our Privacy Policy.