Introduction to ML Classification Models using scikit-learn

Introduction to ML Classification Models using scikit-learn

This course will give you a fundamental understanding of Machine Learning overall with a focus on building classification models. Basic ML concepts of ML are explained, including Supervised and Unsupervised Learning; Regression and Classification; and Overfitting. There are 3 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.

The Intro to ML Classification Models course is meant for developers or data scientists (or anybody else) who knows basic Python programming and wishes to learn about Machine Learning, with a focus on solving the problem of classification. 

An overview of Machine Learning with hands-on implementation of classification models using Python's scikit-learn

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What you will learn

• Have a broad understanding of ML and hands on experience with building classification models using Support Vector Machines, Decision Trees and Random Forests in Python's scikit-learn

Rating: 3.9

Level: Intermediate Level

Duration: 2 hours

Instructor: Loony Corn