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The Applied Artificial Intelligence Workshop
The Applied Artificial Intelligence Workshop

The Applied Artificial Intelligence Workshop: Start working with AI today, to build games, design decision trees, and train your own machine learning models

By Anthony So , William So , Zsolt Nagy
$38.99
Book Jul 2020 420 pages 1st Edition
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Product Details

Publication date : Jul 22, 2020
Length 420 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781800205819
Category :
Data
Languages :
Python (Intermediate)
Concepts :
Artificial Intelligence
Tools :
Jupyter (Intermediate)
Table of content icon View table of contents Preview book icon Preview Book

The Applied Artificial Intelligence Workshop

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.

Introduction

In the previous chapter, you were introduced to the fundamentals of Artificial Intelligence (AI), which helped you create the game Tic-Tac-Toe. In this chapter, we will be looking at regression, which is a machine learning algorithm that can be used to measure how closely related independent variable(s), called features, relate to a dependent variable called a label.

Linear regression is a concept with many applications a variety of fields, ranging from finance (predicting the price of an asset) to business (predicting the sales of a product) and even the economy (predicting economy growth).

Most of this chapter will deal with different forms of linear regression, including linear regression with one variable, linear regression with multiple variables, polynomial regression with one variable, and polynomial regression with multiple variables. Python provides lots of forms of support for performing regression operations and we will also be looking at these later on...

Linear Regression with One Variable

A general regression problem can be defined with the following example. Suppose we have a set of data points and we need to figure out the best fit curve to approximately fit the given data points. This curve will describe the relationship between our input variable, x, which is the data point, and the output variable, y, which is the curve.

Remember, in real life, we often have more than one input variable determining the output variable. However, linear regression with one variable will help us to understand how the input variable impacts the output variable.

Types of Regression

In this chapter, we will work with regression on the two-dimensional plane. This means that our data points are two-dimensional, and we are looking for a curve to approximate how to calculate one variable from another.

We will come across the following types of regression in this chapter:

  • Linear regression with one variable using a polynomial of degree...

Linear Regression with Multiple Variables

In the previous section, we dealt with linear regression with one variable. Now we will learn an extended version of linear regression, where we will use multiple input variables to predict the output.

Multiple Linear Regression

If you recall the formula for the line of best fit in linear regression, it was defined as 20, where 21 is the slope of the line, 22 is the y intercept of the line, x is the feature value, and y is the calculated label value.

In multiple regression, we have multiple features and one label. If we have three features, x1, x2, and x3, our model changes to 23.

In NumPy array format, we can write this equation as follows:

y = np.dot(np.array([a1, a2, a3]), np.array([x1, x2, x3])) + b

For convenience, it makes sense to define the whole equation in a vector multiplication format. The coefficient of 24 is going to be 1:

y = np.dot(np.array([b, a1, a2, a3]) * np.array([1, x1, x2, x3]))

Multiple linear regression...

Polynomial and Support Vector Regression

When performing a polynomial regression, the relationship between x and y, or using their other names, features, and labels, is not a linear equation, but a polynomial equation. This means that instead of the 29 equation, we can have multiple coefficients and multiple powers of x in the equation.

To make matters even more complicated, we can perform polynomial regression using multiple variables, where each feature may have coefficients multiplying different powers of the feature.

Our task is to find a curve that best fits our dataset. Once polynomial regression is extended to multiple variables, we will learn the SVM model to perform polynomial regression.

Polynomial Regression with One Variable

As a recap, we have performed two types of regression so far:

  • Simple linear regression: 30
  • Multiple linear regression: 31

We will now learn how to do polynomial linear regression with one variable. The equation for polynomial...

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...

Summary

In this chapter, we have learned the fundamentals of linear regression. After going through some basic mathematics, we looked at the mathematics of linear regression using one variable and multiple variables.

Then, we learned how to load external data from sources such as a CSV file, Yahoo Finance, and Quandl. After loading the data, we learned how to identify features and labels, how to scale data, and how to format data to perform regression.

We learned how to train and test a linear regression model, and how to predict the future. Our results were visualized by an easy-to-use Python graph plotting library called pyplot.

We also learned about a more complex form of linear regression: linear polynomial regression using arbitrary degrees. We learned how to define these regression problems on multiple variables and compare their performance on the Boston House Price dataset. As an alternative to polynomial regression, we also introduced SVMs as a regression model and...

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Key benefits

  • 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

Description

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.

What you will learn

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

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Product Details

Publication date : Jul 22, 2020
Length 420 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781800205819
Category :
Data
Languages :
Python (Intermediate)
Concepts :
Artificial Intelligence
Tools :
Jupyter (Intermediate)

Table of Contents

8 Chapters
Preface Chevron down icon Chevron up icon
About the Book
1. Introduction to Artificial Intelligence Chevron down icon Chevron up icon
1. Introduction to Artificial Intelligence
Introduction
Fields and Applications of AI
AI Tools and Learning Models
The Role of Python in AI
Python for Game AI
Heuristics
Pathfinding with the A* Algorithm
Introducing the A* Algorithm
Game AI with the Minmax Algorithm and Alpha-Beta Pruning
The Minmax Algorithm
DRYing Up the Minmax Algorithm – the NegaMax Algorithm
Summary
2. An Introduction to Regression Chevron down icon Chevron up icon
2. An Introduction to Regression
Introduction
Linear Regression with One Variable
Linear Regression with Multiple Variables
Polynomial and Support Vector Regression
Support Vector Regression
Summary
3. An Introduction to Classification Chevron down icon Chevron up icon
3. An Introduction to Classification
Introduction
The Fundamentals of Classification
Data Preprocessing
The K-Nearest Neighbors Classifier
Classification with Support Vector Machines
Summary
4. An Introduction to Decision Trees Chevron down icon Chevron up icon
4. An Introduction to Decision Trees
Introduction
Decision Trees
The Confusion Matrix
Random Forest Classifier
Summary
5. Artificial Intelligence: Clustering Chevron down icon Chevron up icon
5. Artificial Intelligence: Clustering
Introduction
Defining the Clustering Problem
Clustering Approaches
The K-Means Algorithm
The Mean Shift Algorithm
Clustering Performance Evaluation
Summary
6. Neural Networks and Deep Learning Chevron down icon Chevron up icon
6. Neural Networks and Deep Learning
Introduction
Artificial Neurons
Neurons in TensorFlow
Neural Network Architecture
Activation Functions
Forward Propagation and the Loss Function
Backpropagation
Optimizers and the Learning Rate
Regularization
Deep Learning
Computer Vision and Image Classification
Recurrent Neural Networks (RNNs)
Summary
Appendix Chevron down icon Chevron up icon
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
6. Neural Networks and Deep Learning

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Authors (9)

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Profile icon Ashish Pratik Patil
See other products by Ashish Pratik Patil
Profile icon Zsolt Nagy
LinkedIn Github
Zsolt Nagy is an engineering manager in an ad tech company heavy on data science. After acquiring his MSc in inference on ontologies, he used AI mainly for analyzing online poker strategies to aid professional poker players in decision making. After the poker boom ended, he put extra effort into building a T-shaped profile in leadership and software engineering.
See other products by Zsolt Nagy
Profile icon Shantanu Shivrup Pathak
LinkedIn
Shantanu Pathak has given training on machine learning in Amdocs, Tech-Mahindra, Honeywell and many other companies. He has been working in this field for more than 6 years out of a total 12 years of his professional experience. Also, he is pursuing a Ph.D. ( CSE) in the topic of Machine Learning.
See other products by Shantanu Shivrup Pathak
Profile icon Subhranil Roy
LinkedIn Github
Contacted for Applied Unsupervised Learning with R on May 8, 2019, by Sneha Shinde Contracted TR by Sneha Shinde on Jan 17, 2019
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Profile icon William So
LinkedIn
William So is a Data Scientist with both a strong academic background and extensive professional experience. He is currently the Head of Data Science at Douugh and also a Lecturer for Master of Data Science and Innovation at the University of Technology Sydney. During his career, he successfully covered the end-end spectrum of data analytics from ML to Business Intelligence helping stakeholders derive valuable insights and achieve amazing results that benefits the business. William is a co-author of the "The Applied Artificial Intelligence Workshop" published by Packt.
Read more
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Profile icon Anthony So
LinkedIn
Anthony So is a renowned leader in data science. He has extensive experience in solving complex business problems using advanced analytics and AI in different industries including financial services, media, and telecommunications. He is currently the chief data officer of one of the most innovative fintech start-ups. He is also the author of several best-selling books on data science, machine learning, and deep learning. He has won multiple prizes at several hackathon competitions, such as Unearthed, GovHack, and Pepper Money. Anthony holds two master's degrees, one in computer science and the other in data science and innovation.
Read more
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Profile icon John Wesley Doyle
Contacted on 02 November 2017 by Vijay Rai for C# 7 and .NET Core Projects
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Profile icon Mr. Fabio M. Soares
LinkedIn Github
Fbio M. Soares is currently a PhD candidate at the Federal University of Par (Universidade Federal do Par - UFPA), in northern Brazil. He is very passionate about technology in almost all fields, and designs neural network solutions since 2004 and has applied this technique in several fields like telecommunications, industrial process control and modeling, hydroelectric power generation, financial applications, retail customer analysis and so on. His research topics cover supervised learning for data-driven modeling. As of 2017, he is currently carrying on research projects with chemical process modeling and control in the aluminum smelting and ferronickel processing industries, and has worked as a lecturer teaching subjects involving computer programming and artificial intelligence paradigms. As an active researcher, he has also a number of articles published in English language in many conferences and journals, including four book chapters.
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Profile icon Swanand Bagve
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