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Machine Learning with scikit-learn Quick Start Guide

You're reading from Machine Learning with scikit-learn Quick Start Guide

Product type Book

Published in Oct 2018

Publisher Packt

ISBN-13 9781789343700

Pages 172 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Kevin Jolly

Table of Contents (10) Chapters

Preface

Introducing Machine Learning with scikit-learn

Predicting Categories with K-Nearest Neighbors

Predicting Categories with Logistic Regression

Predicting Categories with Naive Bayes and SVMs

Predicting Numeric Outcomes with Linear Regression

Classification and Regression with Trees

Clustering Data with Unsupervised Machine Learning

Performance Evaluation Methods

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Feature engineering for optimization

Engineering the features in your dataset is a concept that is fundamentally used to improve the performance of your model. Fine-tuning the features to the algorithm's design is beneficial, because it can lead to an improvement in accuracy, while reducing the generalization errors at the same time. The different kinds of feature engineering techniques for optimizing your dataset that you will learn are as follows:

Scaling
Principal component analysis

Scaling

Scaling is the process of standardizing your data so that the values under every feature fall within a certain range, such as -1 to +1. In order to scale the data, we subtract each value of a particular feature with the mean...

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Authors (1)

Kevin Jolly

Kevin Jolly is a formally educated data scientist with a master's degree in data science from the prestigious King's College London. Kevin works as a statistical analyst with a digital healthcare start-up, Connido Limited, in London, where he is primarily involved in leading the data science projects that the company undertakes. He has built machine learning pipelines for small and big data, with a focus on scaling such pipelines into production for the products that the company has built. Kevin is also the author of a book titled Hands-On Data Visualization with Bokeh, published by Packt. He is the editor-in-chief of Linear, a weekly online publication on data science software and products.

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