Hands-on Scikit-Learn for Machine Learning Applications

Hands-on Scikit-Learn for Machine Learning Applications

Hands-on Scikit-Learn for Machine Learning Applications


  • Author: David Paper
  • ISBN: 1484253728
  • Year: 2019
  • Pages: 242
  • Language: English
  • File size: 3.2 MB
  • File format: PDF
  • Category: Programming

 

Book Description:

Aspiring data science specialists can find out the Scikit-Learn library together with the essentials of machine learning this book. The book combines the Anaconda Python distribution together with the favorite Scikit-Learn library to show a wide range of supervised and unsupervised machine learning algorithms. Care is taken to walk you through the principles of machine learning via clear examples written in Python which you can try out and experiment with at home in your own machine.

All applied math and programming skills necessary to master the articles are covered in this publication. An in-depth understanding of object-oriented programming isn’t required as working and finish examples are provided and explained. Coding examples are in-depth and complex when necessary. They’re also concise, accurate, and complete, and complement the machine learning concepts introduced. Working the illustrations will help to build the skills required to comprehend and apply complex machine learning algorithms.

Hands-on Scikit-Learn for Machine Learning Applications is a great starting point for people pursuing a career in machine learning. Pupils of the book will learn the principles that are a prerequisite to competency. Readers will be exposed to the Anaconda distribution of Python that’s designed particularly for data science professionals and will build skills in the favorite Scikit-Learn library which provides many machine learning software in the world of Python.

What You Will Learn

  • Work with simple and Intricate datasets common to Scikit-Learn
  • Manipulate information into vectors and matrices for algorithmic processing
  • eventually, become familiar with the Anaconda distribution utilized in data science
  • Employ machine learning Classifiers, Regressors, along with Dimensionality Reduction
  • Song calculations and find the best algorithms for each dataset
  • Load data from and save to CSV, JSON, Numpy, and Pandas formats

The aspiring data scientist yearning to split machine learning via mastering the underlying principles that are sometimes skipped over in the rush to be productive. Some understanding of object-oriented programming and also very basic applied linear algebra will make learning easier, although anyone may gain from this book.

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