Includes index.

Online resource; title from PDF title page (EBSCO, viewed April 9, 2019)

Introduction to machine learning -- Extending Python using NumPy -- Manipulating tabular data using Pandas -- Data visualization using matplotlib -- Getting started with Scikit-learn for Machine Learning -- Supervised learning : linear regression -- Supervised learning : classification using logistic regression -- Supervised learning : classification using support vector machines -- Supervised learning : classification using K-Nearest Neighbors (KNN) -- Unsupervised learning : clustering using K-Means -- Using Azure Machine Learning Studio -- Deploying machine learning models.

Python makes machine learning easy for beginners and experienced developers With computing power increasing exponentially and costs decreasing at the same time, there is no better time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on desktop machines. However, machine learning is not for the faint of heart-it requires a good foundation in statistics, as well as programming knowledge. Python Machine Learning will help coders of all levels master one of the most in-demand programming skillsets in use today. Readers will get started by following fundamental topics such as an introduction to Machine Learning and Data Science. For each learning algorithm, readers will use a real-life scenario to show how Python is used to solve the problem at hand. - Python data science-manipulating data and data visualization - Data cleansing - Understanding Machine learning algorithms - Supervised learning algorithms - Unsupervised learning algorithms - Deploying machine learning models Python Machine Learning is essential reading for students, developers, or anyone with a keen interest in taking their coding skills to the next level.

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