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

The K-Means Algorithm

The k-means algorithm is a flat clustering algorithm, as mentioned previously. It works as follows:

  • Set the value of k.
  • Choose k data points from the dataset that are the initial centers of the individual clusters.
  • Calculate the distance from each data point to the chosen center points and group each point in the cluster whose initial center is the closest to the data point.
  • Once all the points are in one of the k clusters, calculate the center point of each cluster. This center point does not have to be an existing data point in the dataset; it is simply an average.
  • Repeat this process of assigning each data point to the cluster whose center is closest to the data point. Repetition continues until the center points no longer move.

To ensure that the k-means algorithm terminates, we need the following:

  • A maximum threshold value at which the algorithm will then terminate
  • A maximum number of repetitions of shifting...