With knowledge and information shared by experts, take your first steps towards creating scalable AI algorithms and solutions in Python, through practical exercises and engaging activities

• Learn about AI and ML algorithms from the perspective of a seasoned data scientist
• Get practical experience in ML algorithms, such as regression, tree algorithms, clustering, and more
• Design neural networks that emulate the human brain

You already know that artificial intelligence (AI) and machine learning (ML) are present in many of the tools you use in your daily routine. But do you want to be able to create your own AI and ML models and develop your skills in these domains to kickstart your AI career?

The Applied Artificial Intelligence Workshop gets you started with applying AI with the help of practical exercises and useful examples, all put together cleverly to help you gain the skills to transform your career.

The book begins by teaching you how to predict outcomes using regression. You'll then learn how to classify data using techniques such as k-nearest neighbor (KNN) and support vector machine (SVM) classifiers. As you progress, you'll explore various decision trees by learning how to build a reliable decision tree model that can help your company find cars that clients are likely to buy. The final chapters will introduce you to deep learning and neural networks. Through various activities, such as predicting stock prices and recognizing handwritten digits, you'll learn how to train and implement convolutional neural networks (CNNs) and recurrent neural networks (RNNs).

By the end of this applied AI book, you'll have learned how to predict outcomes and train neural networks and be able to use various techniques to develop AI and ML models.

• Create your first AI game in Python with the minmax algorithm
• Implement regression techniques to simplify real-world data
• Experiment with classification techniques to label real-world data
• Perform predictive analysis in Python using decision trees and random forests
• Use clustering algorithms to group data without manual support
• Learn how to use neural networks to process and classify labeled images

The Applied Artificial Intelligence Workshop is designed for software developers and data scientists who want to enrich their projects with machine learning. Although you do not need any prior experience in AI, it is recommended that you have knowledge of high school-level mathematics and at least one programming language, preferably Python. Although this is a beginner's book, experienced students and programmers can improve their Python skills by implementing the practical applications given in this book.

1. Introduction to Artificial Intelligence
2. An Introduction to Regression
3. An Introduction to Classification
4. An Introduction to Decision Trees
5. Artificial Intelligence: Clustering

1. Neural Networks and Deep Learning

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3. 主要特点
4. 从一个经验丰富的数据科学家的角度来学习人工智能和ML算法
5. 获得ML算法的实践经验，如回归、树形算法、聚类等
6. 设计模拟人脑的神经网络
7. 书中描述
8. 你已经知道，人工智能（AI）和机器学习（ML）存在于你日常使用的许多工具中。但你是否希望能够创建自己的人工智能和ML模型，并发展你在这些领域的技能，以启动你的人工智能事业？
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10. 应用人工智能讲习班让你在实际练习和有用的例子的帮助下开始应用人工智能，所有这些都被巧妙地放在一起，以帮助你获得改变你职业生涯的技能。
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12. 本书首先教你如何使用回归法预测结果。然后，你将学习如何使用k-近邻（KNN）和支持向量机（SVM）分类器等技术进行数据分类。随着你的进展，你将通过学习如何建立一个可靠的决策树模型来探索各种决策树，以帮助你的公司找到客户可能购买的汽车。最后几章将向你介绍深度学习和神经网络。通过各种活动，如预测股票价格和识别手写数字，你将学习如何训练和实现卷积神经网络（CNN）和递归神经网络（RNN）。
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14. 在这本应用人工智能书的最后，你将学会如何预测结果和训练神经网络，并能够使用各种技术来开发人工智能和ML模型。
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16. 你将学到的内容
17. 使用minmax算法在Python中创建你的第一个人工智能游戏
18. 实施回归技术以简化真实世界的数据
19. 实验分类技术以标记真实世界的数据
20. 使用决策树和随机森林在Python中进行预测分析
21. 使用聚类算法对数据进行分组，无需人工支持
22. 学习如何使用神经网络来处理和分类标记的图像
23. 本书适用对象
24. 应用人工智能讲习班是为那些想用机器学习来丰富自己项目的软件开发人员和数据科学家设计的。虽然你不需要有任何人工智能方面的经验，但建议你有高中水平的数学知识和至少一种编程语言，最好是Python。虽然这是一本初学者的书，但有经验的学生和程序员可以通过实施本书给出的实际应用来提高他们的Python技能。
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26. 目录
27. 人工智能简介
28. 回归简介
29. 分类简介
30. 决策树简介
31. 人工智能。聚类
32. 神经网络和深度学习