Article by Ayman Alheraki in November 30 2024 12:32 PM
In the 1980s, around forty years ago, a popular saying emerged: "An illiterate person is one who knows nothing about computers." Before that, the term "illiterate" referred only to those who could neither read nor write.
Today, understanding at least the basics of artificial intelligence (AI), including concepts like machine learning, generating text from pre-trained models, and deep learning, has become a necessity—not just for the general public, but especially for software developers who aren't specialized in AI. The rapid advancements in AI mean that programmers must catch up with this incredible progress before it's too late.
In 1989, I first heard the term artificial intelligence. At that time, it was just a dream. Over the years, the concept evolved significantly, reaching unprecedented heights. From 2016 onwards, there has been an accelerated race in the field—ranging from designing AI-specific processors to developing software, models, concepts, and frameworks that we know today.
Therefore, I strongly encourage every software developer, regardless of their specialization, to learn about AI—at least its fundamental principles and terminology—and explore how to leverage it using all the resources available.
Personally, I neglected this topic for a long time but have finally caught up and started studying it in recent months. I began gathering information and learning the basics in detail. This effort has culminated in a book I’m authoring, titled:
"AI Concepts Using Python"
In this book, I aim to simplify AI concepts as much as possible for programmers and beginners. Currently, I’ve completed six chapters and have organized the contents of the book with the help of modern tools like ChatGPT and advice from experienced AI professionals. The book will span approximately 400 pages, divided into six sections and 17 chapters. It will comprehensively cover the foundational aspects of AI from various angles.
A while ago, I published a 100-page booklet on AI concepts using C++, which received positive feedback. This is especially noteworthy because few programmers in this field rely on C++—except, of course, for advanced applications requiring high efficiency. Interestingly, many Python libraries in this domain are implemented in C++. However, at the beginner and general level, Python remains dominant. This dominance, combined with Python's ease of use, wealth of libraries, and availability of examples, encouraged me to use Python for this book.
Now, I’d like to share the table of contents to gather comments, suggestions, and feedback from interested readers. Note that the book will be available for free.
Why This Book?
The importance of AI in modern times
The role of Python in simplifying learning
Overview of Artificial Intelligence
What is AI?
The difference between AI, Machine Learning, and Deep Learning
Applications of AI in daily life
Book Objectives
Teaching fundamental AI concepts
Providing practical examples in Python
Enabling readers to start building small projects
Definition of Artificial Intelligence
Types of AI: Narrow (ANI), General (AGI), Super (ASI)
Applications of AI
A brief introduction to Python
Popular AI libraries: NumPy, Pandas, Matplotlib
Practical examples for data analysis
Data: The fuel of AI
Algorithms: The essential tools for execution
Mathematical foundations: Linear algebra, probabilities, and calculus
The concept of Machine Learning
Differences between supervised and unsupervised learning
Linear regression
Classification algorithms like K-Nearest Neighbors
Clustering algorithms like K-Means
Practical examples using the Scikit-Learn library
Handling missing data
Splitting data into training and testing sets
Evaluating model performance
Components of neural networks: Layers, nodes, and weights
How neural networks are trained
Practical examples using TensorFlow
Differences between Machine Learning and Deep Learning
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Image classification
Text analysis (Natural Language Processing)
Examples using the Keras library
Converting text into numerical data
Sentiment analysis
Building a simple chatbot
Basics of image processing
Object recognition in images and videos
Applications using the OpenCV library
The concept of reinforcement learning
Building a simple agent to solve a maze
Comparison of tools like TensorFlow, PyTorch, and Scikit-Learn
Installing the Python development environment
Working with Jupyter Notebook
Managing projects using Git
Data bias issues
Transparency and privacy problems
The responsibility of developers and programmers
How to avoid misuse of AI?
AI in quantum computing
Artificial General Intelligence: Is it possible?
A quick review of the main concepts
How to start your own AI project
Resources for further learning and development
List of Libraries Used
Practical Projects for Practice
Additional Resources for Learning
I hope to provide, through this book, valuable and beneficial material for readers and programmers who wish to gain insight into the current concepts of artificial intelligence. I aim to enable them to confidently delve into this field, which today represents the latest technological revolution worldwide.
