Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits
PDF, ePUB
  • eBook:
    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
  • Edition:
    -
  • Categories:
  • Data:
    July 24, 2020
  • ISBN:
    1838826041
  • ISBN-13:
    9781838826048
  • Language:
    English
  • Pages:
    384 pages
  • Format:
    PDF, ePUB

Book Description
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.

Content

Section 1: Supervised Learning
Chapter 1: Introduction to Machine Learning
Chapter 2: Making Decisions with Trees
Chapter 3: Making Decisions with Linear Equations
Chapter 4: Preparing Your Data
Chapter 5: Image Processing with Nearest Neighbors
Chapter 6: Classifying Text Using Naive Bayes

Section 2: Advanced Supervised Learning
Chapter 7: Neural Networks – Here Comes Deep Learning
Chapter 8: Ensembles – When One Model Is Not Enough
Chapter 9: The Y is as Important as the X
Chapter 10: Imbalanced Learning – Not Even 1% Win the Lottery

Section 3: Unsupervised Learning and More
Chapter 11: Clustering – Making Sense of Unlabeled Data
Chapter 12: Anomaly Detection – Finding Outliers in Data
Chapter 13: Recommender System – Getting to Know Their Taste

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