AI Basics: Learn Artificial Intelligence from Scratch (2026)

You will learn how humans use emotions, creativity, common sense, ethics, and real-life experiences to make decisions, while AI uses data, algorithms, pattern recognition, and speed to solve tasks. The course will also explain where AI performs better than humans, where humans remain superior, and how both can work together in the future.

You will learn how AI enables machines to perform tasks that normally require human intelligence, such as learning, problem-solving, language understanding, decision-making, and pattern recognition.

This topic explains the core process behind how Artificial Intelligence systems receive information, analyse data, recognize patterns, and generate useful outputs. In this lesson, we will explore the practical workflow that allows AI to perform tasks such as answering questions, making decisions, identifying images, and supporting decisions.

You will understand the step-by-step process of AI, including data input, processing through algorithms, pattern detection, decision logic, and output generation. The lesson focuses on how AI transforms raw information into meaningful results through structured computational processes.

In this lesson, you will get a beginner-friendly overview of what Machine Learning is and why it is considered the driving force behind many AI systems used today. You will explore how machines can learn from data, identify patterns, and improve performance over time without being explicitly programmed for every task.

Types of Machine Learning introduces the major learning approaches used to build intelligent systems. In this lesson, learners will gain a clear overview of how different Machine Learning methods are designed based on the type of data available and the goal of the task.

Supervised Learning introduces one of the most widely used types of Machine Learning. In this lesson, learners will understand how models are trained using labelled data, where each input is paired with the correct output. This allows the system to learn patterns and make accurate predictions on new data.

You will explore how supervised learning works, why labelled data is important, and how it is commonly used for tasks such as classification and prediction. The lesson also includes simple real-world examples such as spam email detection, house price prediction and medical diagnosis support.

Unsupervised Learning introduces a powerful type of Machine Learning where models learn from unlabeled data without predefined answers. In this lesson,you will understand how AI systems analyze data independently to identify hidden structures, relationships, and meaningful patterns.

You will explore how unsupervised learning is commonly used for tasks such as clustering similar data points, customer segmentation, anomaly detection, and pattern discovery. The lesson also explains why this approach is valuable when labeled data is unavailable or expensive to create.

Reinforcement Learning introduces a unique type of Machine Learning where an AI system learns by interacting with an environment and receiving rewards or penalties based on its actions. In this lesson, you will understand how systems improve decision-making over time through trial and error.

You will explore how reinforcement learning works, including key concepts such as agents, environments, actions, rewards, and goals. The lesson also highlights common applications such as robotics, game-playing AI and autonomous systems.

Semi-Supervised Learning introduces a practical Machine Learning approach that uses both labelled and unlabelled data for training AI models. In this lesson, you will understand how combining a small amount of labelled data with a larger set of unlabelled data can improve learning efficiency and model performance.

You will explore why semi-supervised learning is valuable when labelled data is costly or difficult to obtain, while unlabelled data is more readily available. The lesson also highlights how this method combines the strengths of supervised and unsupervised learning to solve real-world problems more effectively.