• Understanding of Exploratory Data Analysis Fundamentals

• Understanding various concepts of Machine Learning.

• Overview of Machine Learning models being used in Finance.

• Relevant methods of Machine Learning and its applications in banking.

• Machine Learning in Decision Making like credit approval.

• Applications using Natural Language Processing (NLP) in marketing and customer service.

• Examples of use cases of Machine Learning in Finance.

Module 1: Introduction to Machine Learning Fundamentals

* Overview of key machine learning concepts and terminology.

* Exploratory data analysis (EDA) techniques for banking datasets.

* Data pre-processing, cleaning, and feature engineering.

* Introduction to data visualization for insights.

Module 2: Different Learning Models for Banking Applications

* Classification and regression algorithms in banking.

* Loan approval prediction using decision trees and logistic regression.

* Customer churn prediction with support vector machines and neural networks.

* Clustering techniques for customer segmentation.

* Identifying fraud patterns using anomaly detection.

* Market basket analysis for cross-selling opportunities.

Module 3: Natural Language Processing (NLP) & Time Series Analysis in Finance

* Leveraging NLP for sentiment analysis of customer feedback

* Chatbots for customer service and query resolution

* Text analytics for risk assessment and compliance

* Predicting stock prices using time series analysis

* Interest rate forecasting models (LSTM)

* Managing financial market risks using predictive modelling

Module 4: Model Evaluation, Interpretability, and Ethical Considerations

* Performance metrics for evaluating machine learning models.

* Interpreting model decisions and ensuring transparency.

* Addressing biases and ethical challenges in banking AI

* Case studies of successful machine learning integration in banks