Python Machine Learning Cookbook (Paperback)

100 recipes that teach you how to perform various machine learning tasks in the real world

About This Book

• Understand which algorithms to use in a given context with the help of this exciting recipe-based guide
• Learn about perceptrons and see how they are used to build neural networks
• Stuck while making sense of images, text, speech, and real estate? This guide will come to your rescue, showing you how to perform machine learning for each one of these using various techniques

Who This Book Is For

This book is for Python programmers who are looking to use machine-learning algorithms to create real-world applications. This book is friendly to Python beginners, but familiarity with Python programming would certainly be useful to play around with the code.

What You Will Learn

• Explore classification algorithms and apply them to the income bracket estimation problem
• Use predictive modeling and apply it to real-world problems
• Understand how to perform market segmentation using unsupervised learning
• Explore data visualization techniques to interact with your data in diverse ways
• Find out how to build a recommendation engine
• Understand how to interact with text data and build models to analyze it
• Work with speech data and recognize spoken words using Hidden Markov Models
• Analyze stock market data using Conditional Random Fields
• Work with image data and build systems for image recognition and biometric face recognition
• Grasp how to use deep neural networks to build an optical character recognition system

In Detail

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more.

With this book, you will learn how to perform various machine learning tasks in different environments. We'll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you'll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.

You'll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.

Style and approach

You will explore various real-life scenarios in this book where machine learning can be used, and learn about different building blocks of machine learning using independent recipes in the book.

這本書提供了100個食譜，教你如何在現實世界中執行各種機器學習任務。

關於本書：
- 透過這本令人興奮的基於食譜的指南，了解在特定情境下應該使用哪些演算法。
- 學習感知器(perceptrons)並了解如何使用它們來建立神經網路。
- 如果在理解圖像、文字、語音和房地產方面遇到困難，這本指南將幫助你，展示如何使用各種技術來執行機器學習。

本書適合對象：
- 本書適合想要使用機器學習演算法創建實際應用程式的Python程式設計師。
- 本書對於Python初學者來說很友好，但對Python編程有一定熟悉度將對於操作代碼非常有用。

你將學到的內容：
- 探索分類演算法並將其應用於收入等級估計問題。
- 使用預測建模並將其應用於實際問題。
- 了解如何使用無監督學習進行市場分割。
- 探索數據可視化技術，以多種方式與數據互動。
- 找出如何建立推薦引擎。
- 了解如何與文本數據互動並建立模型進行分析。
- 使用隱藏馬可夫模型(Hidden Markov Models)處理語音數據並識別口語詞彙。
- 使用條件隨機場(Conditional Random Fields)分析股票市場數據。
- 處理圖像數據並建立圖像識別和生物特徵識別系統。
- 掌握如何使用深度神經網路建立光學字符識別系統。

詳細內容：
機器學習在現代數據驅動的世界中變得越來越普遍。它在許多領域廣泛應用，如搜索引擎、機器人、自動駕駛汽車等。

通過這本書，你將學習如何在不同環境中執行各種機器學習任務。我們將從探索機器學習的各種實際情境開始，並研究各種構建模塊。在整本書中，你將使用各種機器學習演算法來解決實際問題，並使用Python來實現這些演算法。

你將了解如何處理各種類型的數據，並探索監督學習和無監督學習等機器學習範式之間的差異。我們還涵蓋了一系列回歸技術、分類演算法、預測建模、數據可視化技術、推薦引擎等，並通過實際示例進行說明。

風格和方法：
在本書中，你將探索各種實際情境，其中機器學習可以應用，並通過書中獨立的食譜了解機器學習的不同構建模塊。