Python AI Programming

Prologue

Welcome to Python AI Programming, an opening into the transformational world of Artificial Intelligence as seen through the prism of Python, the language that has come to be synonymous with modern AI development. This book was written with the goal of taking you, the aspiring AI developer, on an illuminating trip through the fundamental aspects of AI, all articulated in the versatile and intuitive language of Python.

Our adventure starts with a detailed overview of Python's principles, revealing how this language is the ideal toolkit for aspiring AI practitioners. As we progress, the domains of Machine Learning and Deep Learning unveil themselves, illustrating how Python's libraries and frameworks are crucial in pioneering advances in these fields. Each chapter advances your AI learning curve, from the fundamentals of data management to the complexity of neural networks.

When you dive into the complexities of Natural Language Processing (NLP), you'll discover Python's strength in parsing human language, a talent that's critical in today's data-driven world. The story then takes you through the intriguing worlds of Computer Vision and Reinforcement Learning, where Python's skills shine in training machines to visually understand the world and make intelligent decisions.

However, as we welcome these technical marvels, we must be mindful of AI ethics. This book teaches you to think ethically as well as code, ensuring that the AI you design is responsible and useful to all.

Remember that this book is more than simply a technical book as you turn each page; it is a companion on your journey to becoming an AI developer. It's about understanding the 'why' as much as the 'how,' about seeing a future in which technology boosts human capacities, fueled by your newfound skills and insights.

Python AI Programming

Navigating fundamentals of ML, deep learning, NLP, and reinforcement learning in practice

Patrick J

Copyright © 2024 by GitforGits

All rights reserved. This book is protected under copyright laws and no part of it may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without the prior written permission of the publisher. Any unauthorized reproduction, distribution, or transmission of this work may result in civil and criminal penalties and will be dealt with in the respective jurisdiction at anywhere in India, in accordance with the applicable copyright laws.

Published by: GitforGits

Publisher: Sonal Dhandre

www.gitforgits.com

[email protected]

Printed in India

First Printing: January 2024

ISBN: 978-8119177639

Cover Design by: Kitten Publishing

For permission to use material from this book, please contact GitforGits at [email protected].

Content

Preface

Chapter 1: Introduction to Artificial Intelligence

Historical Perspective of AI

Transition to AI Era

AI in Modern World

AI in Daily Life

AI in Business Operations

AI for Decision Making

AI in Innovation and Product Development

AI and Data Analytics

AI in Automation and Efficiency

AI in Healthcare

Key Concepts in Artificial Intelligence

Understanding Machine Learning

Deep Learning

Applications of Deep Learning

Python for AI

NumPy and Pandas for Data Handling

Matplotlib and Seaborn for Data Visualization

Scikit-Learn for Machine Learning

TensorFlow and PyTorch for Deep Learning

Keras for Neural Networks

Setting up Python and AI Environment

Installing and Configuring Python on Windows

Installing TensorFlow and Keras

Coffee Preference Prediction App Overview

App Functionalities

Dataset for Coffee Model

Generating Dataset

Understanding AI Project Lifecycle

Idea and Conceptualization

Data Collection and Preparation

Choosing Right Tools and Technologies

Designing the AI Model

Training the AI Model

Model Optimization and Tuning

Integration and Deployment

Testing and Quality Assurance

Ethical Considerations and Compliance

Summary

Chapter 2: Python for AI

Python Basics

Python Data Structures

Functions for Modularity

Loops

Conditional Statements

Error Handling

File Handling

Data Analysis Overview

Pandas at a Glance

Data Analysis with Pandas

Introduction to NumPy

Numerical Computations with NumPy

Sample Program: Using Numpy and Pandas

Data Visualization Overview

Introduction to Matplotlib

Introduction to Seaborn

Setting Up Matplotlib and Seaborn

Basic Line Plot using Matplotlib

Creating a Heatmap using Seaborn

Bar Plot using Seaborn

Pair Plot using Seaborn

Error Handling in Python

Common Errors

Best Practices

Summary

Chapter 3: Data as Fuel for AI

Role of Data

Quality and Diversity of Data

Data in AI Applications

Future Landscape of Data

Data Collection for AI

Traditional Data Collection Methods

Digital Data Collection Methods

Advanced Data Collection Techniques

Automated Data Collection

Implementing Automated Data Collection

Understanding Data Cleaning

Steps in Data Cleaning

Handling Missing Values

Correcting Inconsistencies

Removing Duplicates

Dealing with Outliers

Error Correction

Data Transformation

Preprocessing Methods

Purpose of Data Preprocessing

Methods of Data Preprocessing

Data Preprocessing on Coffee App Data

Exploratory Data Analysis

Importance of EDA

Performing EDA

Data Transformation

Data Transformation Techniques

Feature Engineering

Importance of Feature Engineering

Techniques in Feature Engineering

Summary

Chapter 4: Machine Learning Foundations

Machine Learning Overview

Machine Learning's Contribution to AI

The Impact of Machine Learning

Supervised Learning Process

Exploring Unsupervised Learning

Understanding Unsupervised Learning

Unsupervised Learning Process

Unsupervised Learning in Practice

ML Algorithms Overview

Decision Trees

K-Means Clustering

Sample Program: Applying K-Means Clustering

Decision Trees vs. K-Means Clustering

Model Training

Understanding Model Training

Training K-Means Model on Coffee Data

Sample Program: Visualize Training of K-Means

Overfitting and Underfitting

Understanding Overfitting

Understanding Underfitting

Significance of Overfitting and Underfitting

Cross-Validation Technique

Understanding Cross-Validation

Types of Cross-Validation

Practical Application on K-Means Model

Hyperparameter Tuning

Overview

Hyperparameters in K-Means Clustering

Applying Hyperparameters

Summary

Chapter 5: Essentials of Deep Learning

Overview

Neural Networks

What Are Neural Networks?

How Do Neural Networks Work?

Types of Neural Networks

Building Neurons and Layers

Understanding Layers and Neurons in Neural Networks

Sample Program: Building Neural Network

Neural Network Components

Activation Functions

Loss Functions

Optimizers

Coding Activation Functions, Loss Function, and Optimizer

Exploring CNNs

Understanding Convolutional Neural Networks (CNNs)

Designing a CNN

Explore RNNs

Understanding Recurrent Neural Networks (RNNs)

Designing an RNN

Train Neural Nets (NNs)

Training Deep Learning Model

Coffee Cup Image Classification using CNN

Training Word Prediction using RNN

Fine-tuning Models

Number of Hidden Layers

Number of Neurons per Hidden Layer

Learning Rate

Batch Size

Sample Program: Fine Tuning CNN Model

Summary

Chapter 6: NLP and Computer Vision

Natural Language Processing Overview

NLP Dataset

Defining the NLP Dataset

Generating Dataset

Text Preprocessing

Tokenization

Lowercasing

Removing Punctuation and Special Characters

Removing Stop Words

Stemming and Lemmatization

Performing Preprocessing on the Dataset

Tokenization

Understanding Tokenization

Process of Tokenization

Tokenizing Dataset

Vectorization Approach

One-Hot Encoding

Bag of Words (BoW)

Bag of N-Grams

Term Frequency-Inverse Document Frequency (TF-IDF)

Sample Program: Applying BoW and TF-IDF

Word Embeddings

Understanding Word Embeddings

Popular Word Embedding Models

Sample Program: Applying Word Embeddings

Visualize Word Embeddings

Introduction to Computer Vision

Brief Understanding

Applications of Computer Vision

Computer Vision Model

Image Processing

Overview

Image Processing Procedure

Sample Program: Using OpenCV

Using CNN for Image Processing

Summary

Chapter 7: Hands-on Reinforcement Learning

Introduction

Sequential Decision Making

Key Components of Sequential Decision Making

Action Values and Estimation Algorithm

Action Values

Estimation Algorithms for Action Values

Q-Learning

Markov Decision Process (MDP)

Translating a Problem into MDP

Sample Program: Creating an MDP

Rewards and Tasks

Overview

Michael Littman's Hypothesis on Reward

Continuing Tasks

Episodic Tasks

Reinforcement Learning Policies

Concept of Policy

Specifying Policies

Values and Bellman Equation

What are Value Functions?

The Bellman Equation

Dynamic Programming (DP)

What is DP?

Dynamic Programming Algorithms

Sample Program: Policy Evaluation

Constructing Algorithm

Value Iteration Algorithm Overview

Sample Program: Implement Value Iteration

Summary

Chapter 8: Ethics to AI

Ethics in Technology

AI Ethical Framework (EAAI)

Bias

Fairness

Transparency

Responsibility

Interpretability

Responsible AI

Pillars of Responsible AI

Impact of Responsible AI

Trustworthy AI

Understanding Concept

Trustworthy AI vs. Responsible AI

Enabling Trustworthy AI

Impact/Value for Businesses

Summary

Index

Preface

This book aspires young graduates and programmers to become AI engineers and enter the world of artificial intelligence by combining powerful Python programming with artificial intelligence. Beginning with the fundamentals of Python programming, the book gradually progresses to machine learning, where readers learn to implement Python in developing predictive models. The book provides a clear and accessible explanation of machine learning, incorporating practical examples and exercises that strengthen understanding.

We go deep into deep learning, another vital component of AI. Readers gain a thorough understanding of how Python's frameworks and libraries can be used to create sophisticated neural networks and algorithms, which are required for tasks such as image and speech recognition. Natural Language Processing is also covered in the book, with fundamental concepts and techniques for interpreting and generating human-like language covered. The book's focus on computer vision and reinforcement learning is distinctive, presenting these cutting-edge AI fields in an approachable manner.

Readers will learn how to use Python's intuitive programming paradigm to create systems that interpret visual data and make intelligent decisions based on environmental interactions. The book focuses on ethical AI development and responsible programming, emphasizing the importance of developing AI that is fair, transparent, and accountable.

In this book you will learn how to:

Explore Python basics and AI integration for real-world application and career advancement.

Experience the power of Python in AI with practical machine learning techniques.

Practice Python's deep learning tools for innovative AI solution development.

Dive into NLP with Python to revolutionize data interpretation and communication strategies.

Simple yet practical understanding of reinforcement learning for strategic AI decision-making.

Uncover ethical AI development and frameworks, and concepts of responsible and trustworthy AI.

Harness Python's capabilities for creating AI applications with a focus on fairness and bias.

Each chapter is designed to improve learning by including practical examples, case studies, and exercises that provide hands-on experience. This book is an excellent starting point for anyone interested in becoming an AI engineer, providing the necessary foundational knowledge and skills to delve into the fascinating world of artificial intelligence.

GitforGits

Prerequisites

Knowing simple python scripting and basics of data science is sufficient to enter the world of artificial intelligence. This book will aspire to make you an eligible professional to enter the world of AI Engineers and Data Scientist.

Codes Usage

Are you in need of some helpful code examples to assist you in your programming and documentation? Look no further! Our book offers a wealth of supplemental material, including code examples and exercises.

Not only is this book here to aid you in getting your job done, but you have our permission to use the example code in your programs and documentation. However, please note that if you are reproducing a significant portion of the code, we do require you to contact us for permission.

But don't worry, using several chunks of code from this book in your program or answering a question by citing our book and quoting example code does not require permission. But if you do choose to give credit, an attribution typically includes the title, author, publisher, and ISBN. For example, Python AI Programming by Patrick J.

If you are unsure whether your intended use of the code examples falls under fair use or the permissions outlined above, please do not hesitate to reach out to us at [email protected]. 

We are happy to assist and clarify any concerns.

Chapter 1: Introduction to Artificial Intelligence

Historical Perspective of AI

Remember the old days when we didn't have smartphones constantly in our hands? That is a bit what it was like before AI. We had computers, but they were more like calculators on steroids. They could crunch numbers, follow instructions, but that is about it. No learning, no adapting – pretty basic, right? Think about those massive computers in the '60s and '70s – room-sized giants with less power than your smartphone. They did specific tasks, and to make them do something, you had to feed them a precise set of instructions. There was no Hey Computer, learn this, it was all about programming every single detail.

Imagine writing a recipe for someone who doesn't know anything about cooking. You'd have to explain every step, right? That is how software was before AI. It couldn't think for itself. Programmers had to spell out everything. If there was a task you didn't foresee, well, the software wouldn't know what to do. We've had data for ages, way before AI. But back then, it was like having a goldmine and not knowing how to mine gold. We stored data in huge databases, mostly using it for record-keeping. It was like having a library of books but never reading them. We didn't have the tools to dig deeper and really understand what all that data could tell us.

We will discuss and understand about work life pre-AI. It was a lot more manual, even with computers. For example, think about a bank in the '80s. They had computers, but a lot of the work, like customer service or data analysis, was done by people, tediously sifting through information. There was a lot of potential for human error, and things took longer.

And then, there is the internet, or rather, the lack of it. The early internet was like a small town – everyone knew each other, and there wasn’t much happening. We didn't have the vast ocean of online data and connectivity we have now. It's hard to imagine, right?

Lastly, think about AI back then. It was more a subject of sci-fi novels and movies than real life. People dreamt of intelligent machines, but it was just that – a dream. The technology, the data processing capabilities, they just