Packt Introduction to ML Classification Models using scikit-learn-XQZT

Packt Introduction to ML Classification Models using scikit-learn-XQZT
English | Size: 994.13 MB
Category: CBTs

This course will give you a fundamental understanding of machine learning with a focus on building classification models. The basic concepts of machine learning (ML) are explained, including supervised and unsupervised learning; regression and classification; and overfitting. There are three lab sections which focus on building classification models using support vector machines, decision trees, and random forests using real data sets. The implementation will be performed using the scikit-learn library for Python

Table of Contents:

INTRODUCTION
WHAT IS ML?
SUPPORT VECTOR MACHINES (SVMS)
DECISION TREES
OVERFITTING – THE BANE OF MACHINE LEARNING
ENSEMBLE LEARNING AND RANDOM FORESTS

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