Hands-On Machine Learning for Finance[video]
Published by Packt
This is the code repository for Machine Learning for Finance [Video]. It contains all the supporting project files necessary to work through the video course from start to finish.
About the Video Course
Machine Learning for Finance is a perfect course for financial professionals entering the fintech domain. It shows how to solve some of the most common and pressing issues facing institutions in the financial industry, from retail banks to hedge funds.
This video course focuses on Machine Learning and covers a range of analysis tools, such as NumPy, Matplotlib, and Pandas. It is packed full of hands-on code simulating many of the problems and providing working solutions.
This course aims to build your confidence and the experience to go ahead and tackle real-life problems in financial analysis. The industry is adopting automatic, data-driven algorithms at a rapid pace, and Machine Learning for Finance gives you the skills you need to be at the forefront.
By the end of this course, you will be equipped with all the tools from the world of Finance, machine learning and deep learning essential for tackling all these pressing issues in the area of Fintech.
What You Will Learn
- Creating and using variables in smart contracts
- How to tackle problems in Fintech and financial investments
- Learn feature engineering, EDA and understanding with regards to financial data
- Build an ANN-based model for predicting the stock prices
- Enhance your Machine Learning skills with ensemble models like random forest and XGBoost
- Enhance your understanding of Neural Networks to build regression-based models
- Learn how to identify fraudulent transactions by building a fraud detection model by using classification models
- Achieve efficient frontier by using features like Sharpe ratios and risk management
Instructions and Navigation
Assumed Knowledge
This course is for financial professionals entering the field who already possess some Python skills and wish to become proficient in machine learning.
Requirements: Basic knowledge of Python, finance, and machine learning
Technical Requirements
SETUP AND INSTALLATION This course uses Anaconda based Python 3.6 installation and Jupiter notebooks that come inbuilt with Anaconda.
Minimum Hardware Requirements For successful completion of this course, students will require the computer systems with at least the following:
OS: Windows 7/8/10 or Mac OS or Linux
Processor: Intel i3 and above
Memory: 8 GB
Storage: 4 to 5 GB
Recommended Hardware Requirements For an optimal experience with hands-on labs and other practical activities, we recommend the following configuration:
OS: Windows 7/8/10 or Mac OS or Linux
Processor: Intel i5 and above
Memory: 12 GB
Storage: 8 to 9 GB
Software Requirements
Operating system: Windows 7/8/10 or Mac OS or Linux
Browser: Chrome/Firefox/Edge
Anaconda with Jupyter notebooks
VS Code/ Spyder/ Pycharm
Keras