Machine Learning Fundamentals: Use Python and scikit-learn to get up and running with the hottest developments in machine learning

Hyatt Saleh

  • 出版商: Packt Publishing
  • 出版日期: 2018-11-29
  • 售價: $1,590
  • 貴賓價: 9.5$1,511
  • 語言: 英文
  • 頁數: 240
  • 裝訂: Paperback
  • ISBN: 1789803551
  • ISBN-13: 9781789803556
  • 相關分類: Python程式語言Machine Learning
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With the flexibility and features of scikit-learn and Python, build machine learning algorithms that optimize the programming process and take application performance to a whole new level

Key Features

  • Explore scikit-learn uniform API and its application into any type of model
  • Understand the difference between supervised and unsupervised models
  • Learn the usage of machine learning through real-world examples

Book Description

As machine learning algorithms become popular, new tools that optimize these algorithms are also developed. Machine Learning Fundamentals explains you how to use the syntax of scikit-learn. You'll study the difference between supervised and unsupervised models, as well as the importance of choosing the appropriate algorithm for each dataset. You'll apply unsupervised clustering algorithms over real-world datasets, to discover patterns and profiles, and explore the process to solve an unsupervised machine learning problem.

The focus of the book then shifts to supervised learning algorithms. You'll learn to implement different supervised algorithms and develop neural network structures using the scikit-learn package. You'll also learn how to perform coherent result analysis to improve the performance of the algorithm by tuning hyperparameters.

By the end of this book, you will have gain all the skills required to start programming machine learning algorithms.

What you will learn

  • Understand the importance of data representation
  • Gain insights into the differences between supervised and unsupervised models
  • Explore data using the Matplotlib library
  • Study popular algorithms, such as k-means, Mean-Shift, and DBSCAN
  • Measure model performance through different metrics
  • Implement a confusion matrix using scikit-learn
  • Study popular algorithms, such as Naive-Bayes, Decision Tree, and SVM
  • Perform error analysis to improve the performance of the model
  • Learn to build a comprehensive machine learning program

Who this book is for

Machine Learning Fundamentals is designed for developers who are new to the field of machine learning and want to learn how to use the scikit-learn library to develop machine learning algorithms. You must have some knowledge and experience in Python programming, but you do not need any prior knowledge of scikit-learn or machine learning algorithms.

Table of Contents

  1. Introduction to sciki-learn
  2. Unsupervised Learning: Real-life Applications
  3. Supervised Learning: Key Steps
  4. Supervised Learning Algorithms: Predict Annual Income
  5. Artificial Neural Networks: Predict of Annual Income
  6. Building Your Own Program