AI & Machine Learning in Finance
This 3-day hands-on course introduces finance professionals to Artificial Intelligence and Machine Learning applications in trading, risk management, credit scoring, and forecasting. Participants will learn how to clean, model, and deploy financial data using Python-based ML frameworks.
Duration:
Fees with SST:
3 Days
RM 4,860.00
The finance industry is evolving rapidly with AI and Machine Learning (ML) transforming how professionals analyze risk, predict markets, and detect anomalies.
This 3-day instructor-led program provides a complete foundation for applying ML techniques to financial use cases such as asset forecasting, fraud detection, sentiment analysis, and credit scoring. Participants will learn to preprocess data, build predictive models, evaluate performance, and deploy solutions using Python frameworks like scikit-learn and TensorFlow.
Through structured exercises and a final capstone project, participants gain real-world experience developing end-to-end AI models for finance — from data preparation to deployment — while addressing key ethical and regulatory considerations.
Module 1: Introduction to AI and ML in Finance
Overview of AI applications, key ML concepts, and popular tools like scikit-learn and TensorFlow.
Module 2: Data Preprocessing for Financial ML
Techniques for cleaning, handling missing data, and engineering financial features.
Module 3: Linear Models for Finance
Build regression models to predict stock prices.
Hands-on: Create a price prediction model using regression.
Module 4: Evaluating ML Models
Measure performance using MAE, RMSE, and R².
Hands-on: Apply cross-validation on financial datasets.
Module 5: Classification Models in Finance
Develop credit scoring and fraud detection systems using decision trees and logistic regression.
Module 6: Time Series Analysis
Perform ARIMA-based forecasting and exponential smoothing for financial predictions.
Module 7: Introduction to Neural Networks
Learn fundamentals of deep learning and applications in high-frequency trading.
Module 8: Ensemble Methods
Use Random Forest and Gradient Boosting for portfolio optimization.
Module 9: Natural Language Processing in Finance
Apply sentiment analysis to financial news using NLTK and spaCy.
Module 10: Deploying ML Models
Build, save, and deploy models using Flask or FastAPI.
Module 11: Ethics and Bias in Financial AI
Address bias, regulation, and ethical deployment practices.
Module 12: Capstone Project and Wrap-Up
Develop and present an end-to-end financial ML project.
Contact Us
Frequently Asked Questions (FAQs)
Do I need a programming background?
Basic Python knowledge is recommended but not mandatory.
Will I build actual ML models?
Yes. Each module includes practical exercises and a capstone project.
Can I apply these techniques in my job immediately?
Yes. The course emphasizes real-world financial applications and workflows.
Does this include neural networks and NLP?
Yes. Deep learning and sentiment analysis modules are covered.
Is finance-specific data used?
Yes. All hands-on activities use relevant financial datasets.