Bootcamps     Information Technology     Data     Data Science

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This course is designed to introduce you to the machine learning basics using python and help you build industry level use case for financial banks - predicting fraud in credit card transaction. This course will help you learn most popular python libraries like scikit learn, pandas, numpy and many more along with theoretical concepts like supervised learning, model building and optimisation, feature engineering and pre-processing. The course will also cover basic machine learning algorithms like Linear Regression, Logistic Regression, Decision Trees and Random Forest.


The course will be 4 hours per day for 4 days and will include combination of theory lectures, virtual games, digital whiteboard, live quizzes, industry information, python hand-on demos, assignments, future reference and notes.


The course includes building live end to end machine learning use case from financial domain where you will experience everything from data processing, feature engineering, model building, model optimisation and deployment using various python libraries and core machine learning concepts to help banks predict transactional fraud model that can save them million of dollars.

Prerequisites

Students looking to attend this course should meet the following prerequisites:

Please ensure you have installed Anaconda before attending the live sesisons.

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Objectives
Built a Financial Fraud System with Python & ML Evaluate & Optimize Models Discuss Decision Trees & Random Forests Describe Feature Pre-Processing and Engineering Describe Linear Regression & Logistic Regression Discuss Machine Learning and Python