Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine learning algorithms in Python

Tarek Amr

ISBN 10: 1838826041 / ISBN 13: 9781838826048

Edité par Packt Publishing, 2020

A propos de cet article

N° de réf. du vendeur I-9781838826048

Détails bibliographiques

• Titre: Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine learning algorithms in Python
• Éditeur: Packt Publishing
• Date de parution: 2020
• Langue: anglais
• ISBN à 10 chiffres: 1838826041
• ISBN à 13 chiffres: 9781838826048
• Reliure: Couverture souple
• État de l'article: New

A propos de ce titre

Synopsis

Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problems

Key Features

• Delve into machine learning with this comprehensive guide to scikit-learn and scientific Python
• Master the art of data-driven problem-solving with hands-on 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 scikit-learn is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with scikit-learn 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 real-life problems. You'll also learn about various key supervised and unsupervised machine learning algorithms using practical examples. Whether it is an instance-based learning algorithm, Bayesian estimation, a deep neural network, a tree-based 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 data-driven approach to provide end-to-end 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.

Table of Contents

1. Introduction to Machine Learning & Scikit-Learn
2. Making Decisions with Trees
3. Making decisions with linear equations
4. Preparing Your Data
5. Image processing with nearest neighbors
6. Text Classification - Not all data exists in tables
7. Neural Networks - Here comes the Deep Learning
8. Ensembles - When one model is not enough
9. The Y is as important as the X
10. Imbalanced Learn - Not even 1% win the lottery
11. Clustering - Grouping data when no correct answers are provided
12. Anomaly Detection - Finding Outliers in Data
13. Recommender System - Learning about users' taste from their previous interactions

À propos de l?auteur

Tarek Amr has 8 years of experience in data science and machine learning. After finishing his postgraduate degree at the University of East Anglia, he worked in a number of startups and scale-up companies in Egypt and the Netherlands. This is his second data-related book. His previous book covered data visualization using D3.js. He enjoys giving talks and writing about different computer science and business concepts and explaining them to a wider audience. He can be reached on Twitter at @gr33ndata. He is happy to respond to all questions related to this book. Feel free to get in touch with him if any parts of the book need clarification or if you would like to discuss any of the concepts here in more detail.