Book Image

Machine Learning with scikit-learn Quick Start Guide

By : Kevin Jolly
Book Image

Machine Learning with scikit-learn Quick Start Guide

By: Kevin Jolly

Overview of this book

Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides. This book teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine learning models. You will learn classification, regression, and clustering techniques to work with different types of datasets and train your models. Finally, you will learn about an effective pipeline to help you build a machine learning project from scratch. By the end of this book, you will be confident in building your own machine learning models for accurate predictions.
Table of Contents (10 chapters)

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...