AI for Finance Online Course

Artificial Intelligence(AI) for Finance Training Course

A lot of solutions to key problems in the financial world require predicting the future patterns in data from the past to make better financial decisions right now. The evolution of modern machine learning methods and tools in recent years in the field of computer vision bring promise of the same progress in other important fields such as financial forecasting.

In this course, you’ll first learn how to quickly get started with ML in finances by predicting the future currency exchange rates using a simple modern machine learning method. In this example, you’ll learn how to choose the basic data preparation method and model and then how to improve them. In the next module, you’ll discover a variety of ways to prepare data and then see how they influence models training accuracy. In the last module, you’ll learn how to find and test a few key modern machine learning models to pick up the best performing one.

After finishing this course, you’ll have a solid introduction to apply ML methods to financial data forecasting.

Course Curriculum

Introduction to Financial Forecasting

• The Course Overview
• What’s Financial Forecasting and Why It’s Important?
• Installing Pandas, Scikit-Learn, Keras, and TensorFlow
• Summary

Predicting Currency Exchange Rates with Multi-Layer Perceptron

• Getting and Preparing the Currency Exchange Data
• Building the MLP Model with Keras
• Training and Testing the Model
• Summary and What’s Next?

Loan Approval Prediction with GradientBoostingClassifier

• Getting and Preparing the Loan Approval Data
• Creating, Training, Testing, and Using a GradientBoostingClassfier Model
• Summary and What’s Next?

Detecting Fraud in Financial Services Using Extreme GradientBoostingClassifier

• Getting and Preparing Financial Fraud Data
• Creating, Training, and Testing XGBoost Model
• Summary and What’s Next?

Forecasting Stock Prices Using Long-Short Term Memory Network

• Getting and Preparing the Stock Prices Data
• Building the LSTM Model with Keras
• Training and Testing the Model
• Summary and What’s Next?