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Key benefits

Handle a variety of machine learning tasks effortlessly by leveraging the power of scikit-learn

Perform supervised and unsupervised learning with ease, and evaluate the performance of your model

Practical, easy to understand recipes aimed at helping you choose the right machine learning algorithm

Description

Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility, and within the Python data space, scikit-learn is the unequivocal choice for machine learning. This book includes walk throughs and solutions to the common as well as the not-so-common problems in machine learning, and how scikit-learn can be leveraged to perform various machine learning tasks effectively. The second edition begins with taking you through recipes on evaluating the statistical properties of data and generates synthetic data for machine learning modelling. As you progress through the chapters, you will comes across recipes that will teach you to implement techniques like data pre-processing, linear regression, logistic regression, K-NN, Naïve Bayes, classification, decision trees, Ensembles and much more. Furthermore, you’ll learn to optimize your models with multi-class classification, cross validation, model evaluation and dive deeper in to implementing deep learning with scikit-learn. Along with covering the enhanced features on model section, API and new features like classifiers, regressors and estimators the book also contains recipes on evaluating and fine-tuning the performance of your model. By the end of this book, you will have explored plethora of features offered by scikit-learn for Python to solve any machine learning problem you come across.

Who is this book for?

Data Analysts already familiar with Python but not so much with scikit-learn, who want quick solutions to the common machine learning problems will find this book to be very useful. If you are a Python programmer who wants to take a dive into the world of machine learning in a practical manner, this book will help you too.

What you will learn

Build predictive models in minutes by using scikit-learn

Understand the differences and relationships between Classification and Regression, two types of Supervised Learning.

Use distance metrics to predict in Clustering, a type of Unsupervised Learning

Find points with similar characteristics with Nearest Neighbors.

Use automation and cross-validation to find a best model and focus on it for a data product

Choose among the best algorithm of many or use them together in an ensemble.

Create your own estimator with the simple syntax of sklearn

Explore the feed-forward neural networks available in scikit-learn

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

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Frequently bought together

$43.99

$43.99

scikit-learn : Machine Learning Simplified

$99.99

Total $ 187.97

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Jose Luis Ramirez Nov 28, 2017

I've been working with python in my AI projects and I came up upon this great book of recipes that I can use quickly and practically in every stage of my developments. It is ease to use right away and it has reference to the enough amount of theory so you don't have to go to search around for extra info. The book introduces neural networks in a simple way and it has robust OOP for more complex AI projects.

Amazon Verified review

Rex Jones May 03, 2018

Excellent reference for basic modeling in Python using scikit. Julian Avila has created a great reference that I use it in my class. It's easy to incorporate reading assignments from this book.

Miss S Betts Apr 22, 2020

Purchase book.Open in cloud reader and start flicking through - and the reader starts displaying the word in a single column down the centre of the screen. Can't find settings to stop it.

scikit-learn Cookbook , Second Edition: Over 80 recipes for machine learning in Python with scikit-learn , Second Edition

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