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

Support Vector Regression

SVMs are binary classifiers and are usually used in classification problems (you will learn more about this in Chapter 3, An Introduction to Classification). An SVM classifier takes data and tries to predict which class it belongs to. Once the classification of a data point is determined, it gets labeled. But SVMs can also be used for regression; that is, instead of labeling data, it can predict future values in a series.

The SVR model uses the space between our data as a margin of error. Based on the margin of error, it makes predictions regarding future values.

If the margin of error is too small, we risk overfitting the existing dataset. If the margin of error is too big, we risk underfitting the existing dataset.

In the case of a classifier, the kernel describes the surface dividing the state space, whereas, in a regression, the kernel measures the margin of error. This kernel can use a linear model, a polynomial model, or many other possible...