Collins R. Scikit-learn in Details: Deep understanding

Amazon Digital Services LLC, 2018.This book is a guide for you on how to use Scikit-Learn, a machine learning library for Python programming language. The author first helps you know what Scikit-Learn are and how to set it up on your system. You are also guided on how to load datasets into Scikit-Learn. The author has then guided you on how to use the various machine learning algorithms to implement machine learning models of different types with Scikit-Learn. Some of the algorithms that have been discussed include Support Vector Machine (SVM), Linear Regression, K-Nearest Neighbors and K-Means. In all these, practical examples have been given, hence you will know how to implement models and use them for making predictions.
Machine learning is one of the fields of artificial intelligence. It is through machine learning that computers can learn from data. Learning from data involves extracting patterns, trends and relationships from the data which can in turn be used for decision making. This is key for business success. Scikit-learn is a machine learning library developed for Python programming language. The library has support for numerous algorithms that are popular in machine learning. Scikit-learn come with numerous functions that can be used for data analysis. This book guides you on how to use Scikit-learn for machine learning in Python. Scikit-learn was designed to be used with other Python libraries including Numpy and SciPy.
The content is:
Getting Started with Scikit-learn
Support Vector Machines in Scikit-learn
Scikit-Learn Linear Regression
Scikit-Learn k-Nearest Neighbors Classifier
K-Means Clustering With Scikit-Learn
Subjects include: python programming language, python, linear regression book, scikit-learn, scikit-learn and tensorflow, support vector machine, linear regression, k-nearest neighbor, k-means, kernel, linear regression models, data visualisation, linear regression analysis, linear regression machine learning.