Book Image

The Applied Artificial Intelligence Workshop

By : Anthony So, William So, Zsolt Nagy
Book Image

The Applied Artificial Intelligence Workshop

By: Anthony So, William So, Zsolt Nagy

Overview of this book

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 will 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.
Table of Contents (8 chapters)
Preface

Summary

In this chapter, we have seen how AI can be used to enhance or substitute human abilities such as to listen, speak, understand language, store and retrieve information, think, see, and move.

Then, we moved on to learning about intelligent agents and the way they interact with the environment, solving a problem in a seemingly intelligent way to pursue a goal.

Then, we introduced Python and learned about its role in AI. We looked at a few important Python libraries for developing AI and prepared data for the intelligent agents. We then created a tic-tac-toe game based on predefined rules. We quantified these rules into a number, a process that we call heuristics. We learned how to use heuristics in the A* search algorithm to find an optimal solution to a problem.

Finally, we got to know about the Minmax and NegaMax algorithms so that the AI could win two-player games. In the next chapter, you will be introduced to regression.