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  • Book Overview & Buying The Applied Artificial Intelligence Workshop
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The Applied Artificial Intelligence Workshop

The Applied Artificial Intelligence Workshop

By : Anthony So , William So , Nagy
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The Applied Artificial Intelligence Workshop

The Applied Artificial Intelligence Workshop

5 (1)
By: Anthony So , William So , 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)
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Preface

2. An Introduction to Regression

Overview

In this chapter, you will be introduced to regression. Regression comes in handy when you are trying to predict future variables using historical data. You will learn various regression techniques such as linear regression with single and multiple variables, along with polynomial and Support Vector Regression (SVR). You will use these techniques to predict future stock prices from a stock price data. By the end of this chapter, you will be comfortable using regression techniques to solve practical problems in a variety of fields.

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