Apply AI & Machine Learning to Financial Forecasting

Beginner Level
2 weeks to complete
Flexible Schedule

Board Infinity

What You’ll Learn

Build and apply regression, time series, and clustering models for real financial forecasting scenarios

Engineer advanced financial features such as lag variables, rolling statistics, volatility metrics, and technical indicators

Evaluate and validate models using cross-validation, walk-forward validation, and error metrics like MAE, RMSE, and MAPE

Implement end-to-end AI workflows for stock prediction, credit risk modeling, portfolio analytics, and sentiment analysis

Skills You’ll Gain

Time Series Analysis and Forecasting Financial Data Financial Forecasting Machine Learning Algorithms Forecasting Statistical Machine Learning Credit Risk Machine Learning Methods Verification And Validation Feature Engineering Market Data Model Evaluation Machine Learning Data Preprocessing Model Training Applied Machine Learning Predictive Modeling Regression Analysis

Shareable Certificate

Earn a shareable certificate to add to your LinkedIn profile.

Develop Your Specialized Knowledge

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

There are 4 modules in this course

Learn core machine learning models used in financial prediction, including regression, time series, and clustering techniques. Understand how different ML models are applied to financial datasets and how to interpret outputs for decision-making.

Explore feature engineering techniques that significantly improve forecasting accuracy. Learn how to transform raw financial data into model-ready datasets using lag features, rolling statistics, volatility metrics, and seasonal indicators.

Implement structured validation frameworks such as train-test splits, cross-validation, and walk-forward validation specifically adapted for time-dependent financial data, evaluate models using MAE, RMSE, and MAPE, and apply techniques to detect overfitting, data leakage, and instability in volatile market environments.

Apply end-to-end ML workflows to real financial use cases including stock trend prediction, credit risk modeling, fraud detection, and portfolio analytics, and leverage generative AI tools for sentiment analysis, financial news interpretation, and automated insight generation to support strategic decision-making.