Book description
Integrate scikitlearn with various tools such as NumPy, pandas, imbalancedlearn, and scikitsurprise and use it to solve realworld machine learning problems
Key Features
 Delve into machine learning with this comprehensive guide to scikitlearn and scientific Python
 Master the art of datadriven problemsolving with handson examples
 Foster your theoretical and practical knowledge of supervised and unsupervised machine learning algorithms
Book Description
Machine learning is applied everywhere, from business to research and academia, while scikitlearn is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to provide handson machine learning solutions with scikitlearn and Python toolkits.
The book begins with an explanation of machine learning concepts and fundamentals, and strikes a balance between theoretical concepts and their applications. Each chapter covers a different set of algorithms, and shows you how to use them to solve reallife problems. You'll also learn about various key supervised and unsupervised machine learning algorithms using practical examples. Whether it is an instancebased learning algorithm, Bayesian estimation, a deep neural network, a treebased ensemble, or a recommendation system, you'll gain a thorough understanding of its theory and learn when to apply it. As you advance, you'll learn how to deal with unlabeled data and when to use different clustering and anomaly detection algorithms.
By the end of this machine learning book, you'll have learned how to take a datadriven approach to provide endtoend machine learning solutions. You'll also have discovered how to formulate the problem at hand, prepare required data, and evaluate and deploy models in production.
What you will learn
 Understand when to use supervised, unsupervised, or reinforcement learning algorithms
 Find out how to collect and prepare your data for machine learning tasks
 Tackle imbalanced data and optimize your algorithm for a bias or variance tradeoff
 Apply supervised and unsupervised algorithms to overcome various machine learning challenges
 Employ best practices for tuning your algorithm's hyper parameters
 Discover how to use neural networks for classification and regression
 Build, evaluate, and deploy your machine learning solutions to production
Who this book is for
This book is for data scientists, machine learning practitioners, and anyone who wants to learn how machine learning algorithms work and to build different machine learning models using the Python ecosystem. The book will help you take your knowledge of machine learning to the next level by grasping its ins and outs and tailoring it to your needs. Working knowledge of Python and a basic understanding of underlying mathematical and statistical concepts is required.
Publisher resources
Table of contents
 Title Page
 Copyright and Credits
 About Packt
 Contributors
 Preface
 Section 1: Supervised Learning
 Introduction to Machine Learning
 Making Decisions with Trees
 Making Decisions with Linear Equations
 Preparing Your Data
 Image Processing with Nearest Neighbors
 Classifying Text Using Naive Bayes
 Section 2: Advanced Supervised Learning

Neural Networks – Here Comes Deep Learning
 Getting to know MLP
 Classifying items of clothing
 Untangling the convolutions
 MLP regressors
 Summary
 Ensembles – When One Model Is Not Enough
 The Y is as Important as the X
 Imbalanced Learning – Not Even 1% Win the Lottery
 Section 3: Unsupervised Learning and More
 Clustering – Making Sense of Unlabeled Data
 Anomaly Detection – Finding Outliers in Data
 Recommender System – Getting to Know Their Taste
 Other Books You May Enjoy
Product information
 Title: HandsOn Machine Learning with scikitlearn and Scientific Python Toolkits
 Author(s):
 Release date: July 2020
 Publisher(s): Packt Publishing
 ISBN: 9781838826048
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