Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python
Authors: David Paper
ISBN-10: 1484253728
ISBN-13: 9781484253724
Edition 版次: 1st ed.
Publication Date 出版日期: 2019-11-18
Print Length 页数: 242 pages
Book Description
By finelybook
Aspiring data science professionals can learn the Scikit-Learn library along with the fundamentals of machine learning with this book. The book combines the Anaconda Python distribution with the popular Scikit-Learn library to demonstrate a wide range of supervised and unsupervised machine learning algorithms. Care is taken to walk you through the principles of machine learning through clear examples written in Python that you can try out and experiment with at home on your own machine.
All applied math and programming skills required to master the content are covered in this book. In-depth knowledge of object-oriented programming is not required as working and complete examples are provided and explained. Coding examples are in-depth and complex when necessary. They are also concise,accurate,and complete,and complement the machine learning concepts introduced. Working the examples helps to build the skills necessary to understand and apply complex machine learning algorithms.
Hands-on Scikit-Learn for Machine Learning Applications is an excellent starting point for those pursuing a career in machine learning. Students of this book will learn the fundamentals that are a prerequisite to competency. Readers will be exposed to the Anaconda distribution of Python that is designed specifically for data science professionals,and will build skills in the popular Scikit-Learn library that underlies many machine learning applications in the world of Python.
What You’ll Learn
Work with simple and complex datasets common to Scikit-Learn
Manipulate data into vectors and matrices for algorithmic processing
Become familiar with the Anaconda distribution used in data science
Apply machine learning with Classifiers,Regressors,and Dimensionality Reduction
Tune algorithms and find the best algorithms for each dataset
Load data from and save to CSV,JSON,Numpy,and Pandas formats
1.Introduction to Scikit-Learn
2.Classification from Simple Training Sets
3.Classification from Complex Training Sets
4.Predictive Modeling Through Regression
5.Scikit-Learn Classifher Tuning from Simple Training Sets
6.Scikit-Learn Classifter Tuning from Complex Training Sets
7.Scikit-Learn Regression Tuning
8.Putting It All Together
