Take a comprehensive and step-by-step approach to understanding machine learning

Key Features

• Discover how to apply the scikit-learn uniform API in all types of machine learning models
• Understand the difference between supervised and unsupervised learning models
• Reinforce your understanding of machine learning concepts by working on real-world examples

Book Description

Machine learning algorithms are an integral part of almost all modern applications. To make the learning process faster and more accurate, you need a tool flexible and powerful enough to help you build machine learning algorithms quickly and easily. With The Machine Learning Workshop, you'll master the scikit-learn library and become proficient in developing clever machine learning algorithms.

The Machine Learning Workshop begins by demonstrating how unsupervised and supervised learning algorithms work by analyzing a real-world dataset of wholesale customers. Once you've got to grips with the basics, you'll develop an artificial neural network using scikit-learn and then improve its performance by fine-tuning hyperparameters. Towards the end of the workshop, you'll study the dataset of a bank's marketing activities and build machine learning models that can list clients who are likely to subscribe to a term deposit. You'll also learn how to compare these models and select the optimal one.

By the end of The Machine Learning Workshop, you'll not only have learned the difference between supervised and unsupervised models and their applications in the real world, but you'll also have developed the skills required to get started with programming your very own machine learning algorithms.

What you will learn

• Understand how to select an algorithm that best fits your dataset and desired outcome
• Explore popular real-world algorithms such as K-means, Mean-Shift, and DBSCAN
• Discover different approaches to solve machine learning classification problems
• Develop neural network structures using the scikit-learn package
• Use the NN algorithm to create models for predicting future outcomes
• Perform error analysis to improve your model's performance

Who this book is for

The Machine Learning Workshop is perfect for machine learning beginners. You will need Python programming experience, though no prior knowledge of scikit-learn and machine learning is necessary.

Table of Contents

1. Introduction to Scikit-Learn
2. Unsupervised Learning - Real-Life Applications
3. Supervised Learning - Key Steps
4. Supervised Learning Algorithms: Predicting Annual Income
5. Artificial Neural Networks: Predicting Annual Income

1. Building Your Own Program

2. 
   
3. 主要特点
4. 了解如何在所有类型的机器学习模型中应用scikit-learn的统一API
5. 了解有监督和无监督的学习模型之间的区别
6. 通过处理真实世界的例子，加强对机器学习概念的理解
7. 书中描述
8. 机器学习算法是几乎所有现代应用的一个组成部分。为了使学习过程更快、更准确，你需要一个足够灵活和强大的工具来帮助你快速、轻松地构建机器学习算法。通过《机器学习工作坊》，你将掌握scikit-learn库，并能熟练地开发聪明的机器学习算法。
9. 
   
10. 机器学习工作坊首先通过分析一个真实世界的批发客户数据集来展示无监督和有监督的学习算法如何工作。一旦你掌握了基础知识，你将使用scikit-learn开发一个人工神经网络，然后通过微调超参数来提高其性能。在研讨会即将结束时，你将研究一家银行的营销活动数据集，并建立机器学习模型，以列出可能认购定期存款的客户。你还将学习如何比较这些模型并选择最佳模型。
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12. 在机器学习工作坊结束时，你不仅会学到有监督和无监督模型之间的区别，以及它们在现实世界中的应用，而且你还会发展出必要的技能，开始为自己的机器学习算法编程。
13. 
    
14. 你将学到什么
15. 了解如何选择一种最适合你的数据集和所需结果的算法
16. 探索现实世界中流行的算法，如K-means、Mean-Shift和DBSCAN。
17. 发现解决机器学习分类问题的不同方法
18. 使用scikit-learn包开发神经网络结构
19. 使用NN算法来创建预测未来结果的模型
20. 进行错误分析以提高模型的性能
21. 本书适用对象
22. 机器学习工作坊》非常适合机器学习初学者。你需要有Python编程经验，不过不需要事先了解scikit-learn和机器学习。
23. 
    
24. 目录
25. Scikit-Learn简介
26. 无监督学习--现实生活中的应用
27. 监督学习--关键步骤
28. 监督学习算法。预测年收入
29. 人工神经网络。预测年收入
30. 建立你自己的程序