Machine Learning with Python: Build & Optimize

EDUCBA follows a Python workflow from data handling and visualization into models and optimization. The published outcomes name NumPy, pandas, Matplotlib and scikit-learn. Regression, classification and clustering are compared alongside preprocessing pipelines, PCA and hyperparameter tuning.

What you will learn

• Prepare and visualize a dataset with Python tools.
• Compare regression, classification and clustering models.
• Use dimensionality reduction and tuning to evaluate improvements.

Who this suits

Learners with basic Python familiarity and introductory data-analysis knowledge; Coursera labels the level mixed.

Before you enroll

Check the package versions and datasets used. Keep preprocessing inside the training workflow and separate the final test set from tuning decisions to reduce misleading evaluation results.

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Put it into practice

Build a preprocessing-and-model pipeline for a small public dataset. Compare a baseline with one tuned alternative using the same held-out evaluation.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

View the curriculum and current access options at Coursera.

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