Machine Learning Fundamentals

In this lesson, you'll learn about one of the fundamental tasks in artificial intelligence — classification. You'll discover how a system chooses among predefined categories, what role clear class definitions play, and how classification results drive real business processes.

This lesson introduces the concept of regression in artificial intelligence, which answers the question "how much will it be?". Using Google Maps' ETA as an example, you'll learn how AI predicts continuous values and how to interpret these predictions when working with businesses and ChatGPT.

In this lesson, you'll learn how Machine Learning systems perceive the world through signals, or features. You'll explore their types, their importance, and how to manage them for effective AI communication, both in recommendation systems and when working with AI assistants.

In this lesson, you'll learn about the concept of ranking, which helps AI systems decide in what order to present information. You'll discover how it differs from classification, why it's multi-objective, and how to use this knowledge for effective communication with ChatGPT.

This lesson explores the fundamental concept of similarity in machine learning, which underlies many AI systems, from product recommendations to AI assistant responses. You'll learn how AI perceives similarity and how you can manage it effectively to achieve desired outcomes.

This lesson introduces the concept of anomaly detection in artificial intelligence. You'll learn how to distinguish normal from unusual cases in data, explore its practical applications, including in the context of ChatGPT, and understand why this task is especially important in fields like finance and security.

In this lesson, you'll learn about the concept of clustering — how AI groups similar data without predefined categories. You'll explore practical examples from Google Photos and ChatGPT, as well as the importance of clustering scale and the human role in this process.

This lesson explains how Machine Learning systems function through a continuous learning cycle. You'll learn about the critical role of feedback in improving AI, distinguish between types of feedback, and understand the active human role in this process.

This lesson explores the critical importance of evaluating artificial intelligence (AI) systems. We'll examine why a single, general metric isn't enough, how to take context into account, and how to build effective evaluation frameworks to measure the real value of AI models.

This lesson explores how the cost and significance of AI errors differ depending on context. You'll learn about the concepts of false positives and false negatives, their impact on AI system design and prompt formulation, so you can better manage risk and achieve optimal outcomes.

This lesson explores the concept of overfitting in artificial intelligence, where a model becomes too attached to past data and loses its ability to generalize to new situations. We'll examine how this problem manifests in AI systems, including ChatGPT, and how it can be avoided through effective prompting.

This lesson explores the concept of data drift in AI systems. You'll learn why model performance can decline over time due to changes in the environment, and how to detect and manage this phenomenon.

This lesson explores how bias can enter artificial intelligence systems through training data. We'll examine various forms of bias, their impact on AI decisions, and effective strategies for managing them, especially when using large language models (LLMs).

This lesson explains how ChatGPT and Claude generate responses — as probabilistic choices rather than deterministic knowledge. You'll learn how prompt precision and context influence outcomes, allowing you to manage communication with AI assistants more effectively.

In this lesson, you'll learn how to balance AI's automation capabilities with human oversight. You'll discover where it's appropriate to fully trust AI and where human involvement is necessary to ensure quality and safety.