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Fundamentals of Machine Learning in Finance
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Fundamentals of Machine Learning in Finance

This course is part of Machine Learning and Reinforcement Learning in Finance.

Course Cost

Free course

Intermediate

Skill Level

16 Hours

Self-paced lessons

This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Machine Learning and Reinforcement Learning in Finance Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.

3.7

21,134 Enrolled

English

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3.7

21,134 Enrolled

English

What you'll learn

  • Implement supervised learning algorithms for financial prediction

  • Apply dimensionality reduction techniques to financial data

  • Develop clustering methods for market analysis

  • Master sequence modeling and reinforcement learning

  • Create practical machine learning solutions for trading strategies

Skills you'll gain

Machine Learning
Financial Analysis
Python Programming
Data Science
Algorithmic Trading
Support Vector Machines
Random Forests
Reinforcement Learning

This course includes:

4.5 Hours PreRecorded video

4 programming assignments

Access on Mobile, Tablet, Desktop

FullTime access

Shareable certificate

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Share your certificate with prospective employers and your professional network on LinkedIn.

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Top companies offer this course to their employees

Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.

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There are 4 modules in this course

This comprehensive course explores the fundamentals of machine learning applications in finance, covering both theoretical concepts and practical implementation. Students learn supervised learning techniques like Support Vector Machines and Random Forests, unsupervised learning methods including PCA and clustering, and an introduction to reinforcement learning. The curriculum emphasizes hands-on experience through Python programming assignments and focuses on real-world financial applications such as credit spread prediction, portfolio construction, and market analysis.

Fundamentals of Supervised Learning in Finance

Module 1 · 4 Hours to complete

Core Concepts of Unsupervised Learning, PCA & Dimensionality Reduction

Module 2 · 4 Hours to complete

Data Visualization & Clustering

Module 3 · 4 Hours to complete

Sequence Modeling and Reinforcement Learning

Module 4 · 4 Hours to complete

Fee Structure

Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Machine Learning and Reinforcement Learning in Finance

Reviews

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Faculties

These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.

Fundamentals of Machine Learning in Finance

Intermediate

Skill Level

16 Hours

Self-paced lessons

Course Cost

Free course

Completion

CERTIFICATE

Frequently asked Questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.