Machine Learning techniques for solving major financial issues

Learn

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.

About

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, MatDescriptionlib, 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.

Features

Sets a foundation of what to follow by teaching visualization and exploratory analysis of financial data, the typical features like RSI and moving average.
Predict stock prices by using Machine Learning models like Linear Regression, Random Forest, XGBoost and neural networks.
Use modern portfolio theory, Sharpe ratio, investment simulation, and machine learning to create a rewarding portfolio of stock investments.

Citación :

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