Machine Learning with Python
by IBM
IBM's Machine Learning with Python course teaches you to build real predictive models using Python and the Scikit-Learn library. It covers the most widely used ML algorithms — classification, regression, clustering, and recommendation systems — and applies them to real datasets from finance, healthcare, and business. For professionals who already work with data and want to step into actual AI model building, this is the most practical IBM starting point.
What you will learn
- Regression models: how to predict numerical outcomes like sales, prices, or risk scores
- Classification: how to train AI to categorise inputs — fraud detection, risk assessment, diagnosis
- Clustering: how AI groups customers, patients, or transactions without being told what to look for
- How to evaluate, compare, and improve ML models using real performance metrics
- How to implement all of this in Python using Scikit-Learn — the industry standard ML library
Who is this for
- Financial analysts and risk professionals who want to build predictive models
- Data professionals ready to step from analytics into actual machine learning
- Python users who want to apply ML to their specific industry data
- Anyone pursuing the IBM Data Science or Applied AI Professional Certificate path
Why we recommend it
- 1IBM-certified — a recognised credential for data and engineering roles worldwide
- 2Hands-on Python throughout — every module builds and evaluates a real model
- 3Covers the algorithms most commonly used in finance, healthcare, and operations
- 4Strong foundation for the IBM Applied AI Certificate or any advanced ML path
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