Intro to Machine Learning | Statistical Pattern Classification - Prof Sebastian Raschka

Comprehensive introduction to machine learning and statistical pattern classification, combining theoretical foundations with practical hands-on experience using Python.

Introduction

Introduction to machine learning for pattern classification, regression analysis, clustering, and dimensionality reduction. Fundamental algorithms and contemporary state-of-the-art algorithms are discussed, with a focus on the evaluation of machine learning models using statistical methods.

Highlights

• Covers fundamental mathematical concepts underlying machine learning and pattern classification algorithms
• Practical use of machine learning algorithms using open source libraries from the Python programming ecosystem
• Includes topics such as supervised learning, tree-based methods, model evaluation, dimensionality reduction, and Bayesian learning

Recommendation

This course is recommended for anyone interested in learning about machine learning and statistical pattern classification, from beginners to experienced practitioners. The course provides a comprehensive introduction to the field, combining theoretical foundations with practical hands-on experience using Python.

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