After the program, you will develop:
AI Basics
Understand what AI is, how it works, and where it appears in life.
AI Ethics
Learn about bias, privacy, deepfakes, and responsible AI in society.
ML & GenAI
Explore machine learning, deep learning, language models.
AI Design
Discover the AI lifecycle: data, model, evaluation, and deployment.
AI Basics
Chapter 1 – What Is Artificial Intelligence?
What is artificial intelligence?
In simple terms, it is the ability of computer systems to imitate aspects of human intelligence to achieve goals.
Artificial intelligence (AI)...
Chapter 2 – Types of AI and Its Potential Future
Artificial Intelligence is a concept that goes beyond technological systems designed to perform specific tasks. Researchers classify AI according to its capabilities in order...
Chapter 3 – The Artificial Intelligence Lifecycle
Chapter 3 - The Artificial Intelligence Lifecycle
*If you're exploring different foundations of AI, our article on AI Categories (ANI, AGI, ASI) explains how various...
Chapter 4 – From Dartmouth to Today: The History of AI
The Historical and Philosophical Legacy of AI
The roots of AI reach back to the earliest moments when humans imagined machines capable of intelligent behavior....
Chapter 5 – The Impact of AI on Our Daily Lives
Artificial intelligence is now so integrated into daily life that we constantly interact with it—often without realizing that an intelligent system is working in...
Chapter 6 – Myths of Artificial Intelligence: What AI Is Not!
Artificial intelligence has become an exciting yet often misunderstood field as technology advances rapidly. Popular culture, cinema, and online speculation tend to portray AI...
How AI Works
Chapter 1 – What Is Machine Learning?
In the past, computers followed rules that were explicitly defined step by step by programmers. Every action needed to solve a problem was already...
Chapter 2 – How Machine Learning Works
Machine Learning Process: The Journey from Data to Knowledge
Machine learning often appears to be an abstract field filled with complex terminology, but its underlying...
Chapter 3 – What Are the Key Performance Metrics in ML?
Performance Metrics in ML: What Do Accuracy, Precision, and Recall Really Mean?
When evaluating machine learning models, the most commonly referenced metric is accuracy —...
Chapter 4 – Types of Machine Learning
Understanding how machine learning works begins with understanding how its learning processes are categorized. All modern AI systems we use today—from recommendation engines to...
Chapter 5 – Introduction to Core ML Algorithms
The Logic Behind Core Algorithms: Understanding How Machine Learning “Thinks”
Machine learning is not simply a “feed data – train model – get prediction” loop....
Chapter 5.1 – What Are Decision Trees?
Decision trees are one of the most intuitive and interpretable machine learning algorithms. They allow a model to make decisions by asking a sequence...
5.2 What Are Support Vector Machines (SVM)?
Support Vector Machines (SVM) are powerful machine learning methods designed to find the most "optimal" line, plane, or hyperplane that separates data into different...
