Szczegóły ebooka

Machine Learning with scikit-learn Quick Start Guide. Classification, regression, and clustering techniques in Python

Machine Learning with scikit-learn Quick Start Guide. Classification, regression, and clustering techniques in Python

Kevin Jolly

Ebook
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.
  • 1. Introducing Machine Learning with scikit-learn
  • 2. Predicting categories with K-Nearest Neighbours
  • 3. Predicting categories with Logistic Regression
  • 4. Predicting categories with Naive Bayes and SVMs
  • 5. Predicting numeric outcomes with Linear Regression
  • 6. Classification & Regression with Trees
  • 7. Clustering data with Unsupervised Machine Learning
  • 8. Performance evaluation methods
  • Tytuł: Machine Learning with scikit-learn Quick Start Guide. Classification, regression, and clustering techniques in Python
  • Autor: Kevin Jolly
  • Tytuł oryginału: Machine Learning with scikit-learn Quick Start Guide. Classification, regression, and clustering techniques in Python
  • ISBN: 9781789347371, 9781789347371
  • Data wydania: 2018-10-30
  • Format: Ebook
  • Identyfikator pozycji: e_15b8
  • Wydawca: Packt Publishing