AI for Finance Online Course

AI for Finance Online Course

Many challenges in the financial world revolve around forecasting future trends based on historical data to make informed decisions today. With the rapid advancement of machine learning—especially in fields like computer vision—similar breakthroughs are now being applied to financial forecasting.

In this course, you’ll begin by building a simple machine learning model to predict future currency exchange rates. This hands-on example will guide you through basic data preparation, model selection, and iterative improvement.

Next, you'll explore various data preparation techniques and examine how each impacts model training and prediction accuracy. Finally, the course will walk you through identifying, testing, and comparing several modern machine learning models to determine the most effective one for your financial data.

By the end of the course, you’ll have a strong foundation in applying machine learning techniques to financial forecasting, giving you the tools to make smarter, data-driven financial decisions.

Who should take this Course?

The AI for Finance Online Course is ideal for finance professionals, data analysts, financial analysts, and students who want to apply artificial intelligence and machine learning techniques to financial modeling, forecasting, risk management, and investment strategies. It’s also suitable for fintech enthusiasts and software developers working in the financial sector. A basic understanding of finance, Python programming, and data analysis is recommended for a productive learning experience.

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?