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Book Overview & Buying
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Table Of Contents
Machine Learning 101 with Scikit-learn and StatsModels
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Machine Learning 101 with Scikit-learn and StatsModels
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Overview of this book
Machine Learning is one of the fundamental skills you need to become a data scientist. It’s the steppingstone that will help you understand deep learning and modern data analysis techniques.
In this course, you’ll explore the three fundamental machine learning topics - linear regression, logistic regression, and cluster analysis. Even neural networks geeks (like us) can’t help but admit that it’s these three simple methods that data science revolves around. So, in this course, we will make the otherwise complex subject matter easy to understand and apply in practice. This course supports statistics theory with practical application of these quantitative methods in Python to help you develop skills in the context of data science.
We’ve developed this course with not one but two machine learning libraries: StatsModels and sklearn. You’ll be eager to complete this course and get ready to become a successful data scientist!
All the code and supporting files for this course are available at https://github.com/PacktPublishing/Machine-Learning-101-with-Scikit-learn-and-StatsModels
Table of Contents (8 chapters)
Introduction
Setting Up the Working Environment
Linear Regression with StatsModels
Linear Regression with Sklearn
Linear Regression - Practical Example
Logistic Regression
Cluster Analysis
Cluster Analysis: Additional Topics
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