E-book details

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

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

Ebook
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.
  • 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
  • Title: Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits. A practical guide to implementing supervised and unsupervised machine learning algorithms in Python
  • Author: Tarek Amr
  • Original title: Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits. A practical guide to implementing supervised and unsupervised machine learning algorithms in Python
  • ISBN: 9781838823580, 9781838823580
  • Date of issue: 2020-07-24
  • Format: Ebook
  • Item ID: e_2aej
  • Publisher: Packt Publishing