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Key benefits

• Leverage Python’s most powerful open-source libraries for deep learning, data wrangling, and data visualization

• Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms

• Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets

Description

Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world’s leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you’ll soon be able to answer some of the most important questions facing you and your organization.

What you will learn

• Explore how to use different machine learning models to ask different questions of your data

• Learn how to build neural networks using Keras and Theano

• Find out how to write clean and elegant Python code that will optimize the strength of your algorithms

• Discover how to embed your machine learning model in a web application for increased accessibility

• Predict continuous target outcomes using regression analysis

• Uncover hidden patterns and structures in data with clustering

• Organize data using effective pre-processing techniques

• Get to grips with sentiment analysis to delve deeper into textual and social media data

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Amazon verified reviews

Gabe 101 Dec 15, 2019

Raschka does a good job, particularly in later editions, of walking through the concepts and code. I have adapted some of his work for hands-on Google Colabs for my course in ML and this remains one of the books I recommend for students.

Amazon Verified review

Linxing Oct 09, 2019

One of the must have machine learning guide for aspiring data scientists. If you are going to pursue a career with a lot of data analysis done in python, you will find this guide book invaluable as compared to other counterparts with more coverage in theories.

Layiwola Feb 04, 2019

Everything was intact. Good book. Good buy. Thank You.

KIRAN RAMA Feb 01, 2018

Overhyped book - looks like he took all the stuff from the scikit-learn package

A. Orr Dec 09, 2017

I did learn a lot about the overall concepts of how machine learning works. Although, I haven't found a way to really benefit use it. This is a tough topic to cover in one book for various abilities of readers. Some readers already know advanced statistics. So, this should be an easy read for those. The rest of us will find it slow going to learn. I wouldn't recommend this book to anyone that doesn't have college level mathematical statistics experience. I think it would have been better to divide the book in two. There are various levels of readers that I feel aren't considered. It just can't be done in one book.

Python Machine Learning: Learn how to build powerful Python machine learning algorithms to generate useful data insights with this data analysis tutorial

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