About me

Howdy, my name is Nir Regev and I hold a Ph.D. in Electrical Engineering. When I am writing this book I have been working in the industry for more than two and a half decades and am a professor in California State Polytechnic University, Pomona (Cal Poly Pomona), teaching courses in the Electrical and Computer Engineering department. I am also a Founder and owner of alephzero.ai, a high-tech company.

As an AI expert and educator, I've witnessed the transformative power of technology education. My journey in AI has been incredibly rewarding, and I want to share that experience with passionate students who might not have access to such resources. My father gave me my first programming book when I was 10 years old, and set me on this path of creating technology. This project is my way of “paying forward” and inspiring the next generation of innovators. The philosophy of "learning by doing" is central to my teaching as a professor at Cal Poly Pomona, and this book embodies that approach, providing students with practical, hands-on exercises to solidify their understanding.

You can read more about my background in my website: https://www.drnirregev.com/

Chapters:

Chapter 5: Unsupervised Learning: Clustering and Dimensionality Reduction

Chapter 6: Neural Networks and Deep Learning

Supplementary material

Syllabus

1. Chapter 1: Introduction to AI and Python
• What is Artificial Intelligence?
• Types of AI and real-world applications
• Introduction to Python programming
• Variables, data types, control structures, and functions
2. Chapter 2: Python Libraries and Data Manipulation
• Introduction to NumPy and Pandas libraries
• Data manipulation and preprocessing
• Handling missing data and feature scaling
3. Chapter 3: Machine Learning Fundamentals
• Types of machine learning: supervised, unsupervised, and reinforcement learning
• Linear algebra review (vectors, matrices, and operations)
• Introduction to scikit-learn library
4. Chapter 4: Supervised Learning: Classification and Regression
• K-Nearest Neighbors (k-NN) algorithm
• Decision Trees and Random Forests
• Linear Regression and Logistic Regression
• Model evaluation metrics
5. Chapter 5: Unsupervised Learning: Clustering and Dimensionality Reduction
• K-Means clustering
• Principal Component Analysis (PCA)
• t-SNE for data visualization
6. Chapter 6: Neural Networks and Deep Learning
• Introduction to artificial neural networks
• Activation functions and backpropagation
• Introduction to TensorFlow and Keras libraries
• Building and training deep learning models
7. Chapter 7: Convolutional Neural Networks (CNNs)
• Understanding CNNs and their applications
• Building CNNs for image classification
• Data augmentation techniques
• Transfer learning and fine-tuning
8. Chapter 8: Recurrent Neural Networks (RNNs) and Natural Language Processing (NLP)
• Introduction to RNNs and Long Short-Term Memory (LSTM)
• Text preprocessing and tokenization
• Word embeddings and sentiment analysis
• Building language models and text generation
9. Chapter 9: Generative Adversarial Networks (GANs)
• Understanding GANs and their components
• Training GANs for image generation
• Exploring different GAN architectures
• Applications of GANs in creative tasks
10. Chapter 10: Reinforcement Learning
• Introduction to reinforcement learning concepts
• Markov Decision Processes (MDPs) and Q-learning
• Building a reinforcement learning agent
• Exploring advanced reinforcement learning algorithms
11. Chapter 11: AI Project: Building a Chatbot
• Designing the chatbot architecture
• Collecting and preprocessing conversational data
• Training a language model for the chatbot
• Integrating the chatbot with a user interface
12. Chapter 12: AI Project: Sentiment Analysis Web App
• Building a sentiment analysis model
• Creating a web app using Flask or Django
• Integrating the sentiment analysis model with the web app
• Deploying the web app to a cloud platform
13. Chapter 13: Future of AI and Emerging Trends
• Recent advancements and breakthroughs in AI
• Emerging trends and research directions
• Potential future applications of AI
• Challenges and opportunities in AI development
14. Appendix A: Python Crash Course
• Quick reference for Python syntax and constructs
• Common Python libraries and tools for AI development
15. Appendix B: Resources and Further Reading
• Recommended books, tutorials, and online courses
• Popular AI conferences and journals
• Open-source AI frameworks and libraries

Who is this book for?

This book is designed for high school students, college students and other beginners who have a basic understanding of Python and are eager to dive into the world of Artificial Intelligence (AI). It is also ideal for programmers looking to expand their knowledge and skills by venturing into AI, as well as software-savvy individuals who want to explore the exciting possibilities of AI. Whether you're a high schooler curious about the latest technological advancements, a programmer aiming to transition into AI, or someone with a technical background interested in learning AI, this book provides a comprehensive, self-taught journey tailored to your needs.

Why is this book free?

Artificial Intelligence (AI) is revolutionizing our world, driving advancements across industries, from healthcare to finance to entertainment. As we stand on the brink of an AI-driven future, it's crucial that the next generation is equipped with the knowledge and skills to thrive in this new landscape. However, access to quality AI education remains a challenge, especially for high school students, college students and other beginners who want to step into the fascinating world of AI. That's where our project comes in. I am creating a free, comprehensive AI book specifically designed for beginners, introducing them to AI concepts using Python in an engaging and approachable way.

Impact:

By providing this resource for free, I aim to:

• Foster a passion for technology and AI among beginners
• Equip them with valuable skills for future careers in tech and other fields
• Bridge the gap in AI education accessibility, ensuring that all students, regardless of their background, have the opportunity to learn about this transformative technology

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