Machine Learning Algorithms

Giuseppe Bonaccorso

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商品描述

Key Features

  • Get started in the field of machine learning with the help of this solid, concept rich, yet highly practical guide.
  • Your one-stop solution for everything that matters in machine learning algorithms, like the types, working, and implementation.
  • Get solid foundation to your entry in machine learning by strengthening your roots i.e the algorithms with this comprehensive guide.

Book Description

In this book you will learn all the important machine learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning and semi-supervised learning. Few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naïve Bayes, K-Means, Random Forest, XGBooster and Feature engineering. In this book you will also learn the working and the practical implementation of these algorithms to resolve your problems. This book will also introduce you to Natural Processing Language and Recommendation systems, which help you to run multiple algorithms simultaneously.

On completion of the book you will understand how to pick machine learning algorithm for clustering, classification, or regression best suited for your problem.

What you will learn

  • Acquaint yourself with important elements of Machine Learning
  • Understand the feature selection and feature engineering process
  • Assess performance and error trade-offs of Linear Regression
  • Build a data model and understand how it works by using different types of algorithms
  • Learn to tune parameters of Support Vector machines
  • Implement clusters to a data set
  • Explore the concept of Natural Processing Language and Recommendation Systems
  • Create a ML architecture from scratch.

商品描述(中文翻譯)

《主要特點》

- 以這本實用的概念豐富指南,幫助您入門機器學習領域。
- 這是您在機器學習算法中所需的一站式解決方案,包括類型、運作方式和實施方法。
- 透過這本全面指南,為您在機器學習領域的起步打下堅實基礎,強化您的根基,即算法。

《書籍描述》

在這本書中,您將學習到在數據科學領域常用的重要機器學習算法。這些算法可用於監督學習、非監督學習、強化學習和半監督學習。本書涵蓋了一些著名的算法,如線性回歸、邏輯回歸、支持向量機、朴素貝葉斯、K-Means、隨機森林、XGBooster和特徵工程。在本書中,您還將學習這些算法的運作方式和實際實施,以解決您的問題。本書還將介紹自然語言處理和推薦系統,幫助您同時運行多個算法。

在閱讀完本書後,您將了解如何選擇最適合您問題的機器學習算法,用於聚類、分類或回歸。

《您將學到什麼》

- 熟悉機器學習的重要元素
- 理解特徵選擇和特徵工程過程
- 評估線性回歸的性能和錯誤折衷
- 通過使用不同類型的算法來構建數據模型並了解其運作方式
- 學習調整支持向量機的參數
- 將聚類應用於數據集
- 探索自然語言處理和推薦系統的概念
- 從頭開始創建機器學習架構。