Python AI Programming: Navigating fundamentals of ML, deep learning, NLP, and reinforcement learning in practice
By Patrick J
()
About this ebook
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 de
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Book preview
Python AI Programming - Patrick J
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
support@gitforgits.com
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 support@gitforgits.com.
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 support@gitforgits.com.
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