Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
11 Hour(s) 14 Minute(s)
Language
English
Taught by
Raj Chhabria
Rating
Course Overview
Master Machine Learning and Python for Quantitative Finance and Learn to Build and Backtest Algo Trading Strategies.
--- WELCOME TO THE COURSE ---
This comprehensive course is designed for anyone who wants to leverage machine learning techniques in finance. Covering essential topics such as Pandas, NumPy, Matplotlib, and Seaborn, participants will gain a solid foundation in data manipulation and visualization, crucial for analyzing financial datasets.
The curriculum delves into key financial concepts, including derivatives, technical analysis, and asset pricing models, providing learners with the necessary context to apply machine learning effectively. Participants will explore various machine learning methodologies, including supervised and unsupervised learning, deep learning techniques, and their applications in developing trading strategies.
A significant focus of the course is on hands-on coding projects that allow learners to implement machine learning algorithms for trading strategies and backtesting. By the end of the course, students will have practical experience in building predictive models using Python.
Additionally, the course introduces Streamlit, enabling participants to create interactive web applications and dashboards to showcase their quantitative models effectively. This integration of machine learning with web development equips learners with the skills to present their findings dynamically.
Whether you are a finance professional or a data enthusiast, this course empowers you to harness the power of machine learning in quantitative finance and algorithmic trading, preparing you for real-world challenges in the financial markets. Join us to transform your understanding of finance through advanced analytics and innovative technology!
Course Content
- 9 section(s)
- 98 lecture(s)
- Section 1 Introduction
- Section 2 Pandas and Numpy
- Section 3 Data Visualisation and Preprocessing
- Section 4 Machine Learning
- Section 5 Deep Learning Essentials for Quant Finance
- Section 6 Financial Markets
- Section 7 Python and Machine Learning for Quant Finance Practical Coding Case Studies
- Section 8 Important Streamlit concepts for building Finance Dashboards and Web Apps
- Section 9 Streamlit Project
What You’ll Learn
- Learn about complete life cycle of a Machine Learning Project from Data Processing to Building ML models to Deployment on WebApps built using Streamlit., You will learn about Complex Financial Market concepts like Derivatives, Asset pricing models, Technical Analysis, etc... in simple terms without any jargons., This course covers essentials of Machine Learning and Deep Learning that will help to get an edge in your Quant Analysis of Financial Data., Learn to build your own Trading Strategies using Machine Learning and Backtest them using Python., You will learn how to quickly build your own Web Apps and Dashboards for your Quant Analysis using Streamlit., This course also has lots of Hands on Coding Projects in Python, Machine Learning, Deep Learning and Streamlit.
Skills covered in this course
Reviews
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SSunidhi .i
It was very informative
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SSquirrel
Good match, not too fast and not too slow.
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WWisdom Ariagbofo
Great Course. I learnt a lot about Machine Learning and how its methods can be applied to the Stock Market.
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IIftekhar Mobin
Yes, it's an excellent course, and the contents are really helpful, but they are labeled a bit for beginners, though I was expecting a little more advanced material.
