Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Subscribe
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
Principles of Machine Learning
Rating: 3.8 out of 5(33 ratings)
939 students

Principles of Machine Learning

Explore core ML concepts and algorithms like supervised learning, concept learning, Find-S, and Candidate Elimination.
Created bySalomi M, Dr.Samsudeen S
Last updated 6/2025
English

What you'll learn

  • Understand well-posed learning problems and key components of a machine learning system.
  • Analyze concept learning as a search problem and apply it to hypothesis spaces.
  • Implement and evaluate the Find-S algorithm for concept learning tasks.
  • Apply the Candidate Elimination algorithm to refine hypothesis spaces based on training data.

Course content

2 sections • 6 lectures • 1h 21m total length

  • Introduction to Machine Learning11:35

    Description:
    This lecture offers a foundational understanding of Machine Learning (ML), covering what ML is, why it matters, and how machines learn from data. Students will be introduced to key terminology, types of learning (supervised, unsupervised, and reinforcement), and basic concepts like datasets, features, labels, and models. Through real-world examples and intuitive explanations, learners will grasp how ML fits into broader AI systems and modern technology applications.

    By the end of this lecture, students will be able to:

    Define machine learning and distinguish it from traditional programming

    Identify and explain the main types of machine learning: supervised, unsupervised, and reinforcement learning

    Understand basic ML workflow components: data, models, training, and evaluation

    Recognize the role of features, labels, and datasets in building ML systems

    Appreciate where and how ML is applied in real-life scenarios

  • well posed learning problems8:30

    Description:
    This lecture focuses on understanding what makes a learning problem "well-posed" in the context of machine learning. It introduces the three essential conditions that define a well-posed learning problem—task, performance measure, and experience—based on Tom Mitchell’s formal definition. Students will analyze various ML scenarios to determine whether they qualify as well-posed problems and learn how to properly frame problems for machine learning solutions.

    By the end of this lecture, students will be able to:

    Explain the concept of a well-posed learning problem in machine learning

    Identify the three key components: task (T), performance measure (P), and experience (E)

    Apply Tom Mitchell’s definition to evaluate real-world ML problems

    Formulate a well-posed learning problem from an ambiguous or loosely defined scenario

    Understand the significance of clearly defining T, P, and E before building an ML model

  • Designing a Learning System8:55

    Description:
    This lecture guides students through the systematic process of designing a machine learning system from the ground up. It covers the essential steps such as defining the problem, selecting the right data, choosing the appropriate learning algorithm, and setting up the performance evaluation metrics. The focus is on understanding the key design decisions and trade-offs involved in building effective and efficient ML systems. Real-world use cases are discussed to illustrate how theoretical concepts are applied in practice.

    By the end of this lecture, students will be able to:

    Break down a real-world problem into a well-defined ML task

    Identify the appropriate inputs (features) and outputs (labels) for the learning system

    Choose suitable data representation and preprocessing techniques

    Select the right learning algorithm based on problem type and data characteristics

    Design an evaluation strategy to measure the system’s performance effectively

    Understand the iterative nature of designing, testing, and improving ML systems

Requirements

  • Basic understanding of high school mathematics (sets, logic, and functions)
  • Familiarity with programming concepts
  • Curiosity to learn how machines can learn from data

Description

This course offers a comprehensive introduction to the fundamental concepts and algorithms that form the backbone of machine learning. It focuses on designing effective learning systems and understanding the theoretical underpinnings that make machine learning possible.

Students will begin with an overview of machine learning, exploring its applications, types, and real-world impact. The course then delves into the essential components of learning systems, emphasizing what makes a learning problem well-posed and how to frame learning tasks appropriately.

Key algorithms such as Find-S and Candidate Elimination will be studied in detail, offering insights into hypothesis space search and version space representation. Learners will also explore the Decision Tree algorithm, understanding how machines make structured decisions based on data-driven patterns and logic.

By the end of this course, students will:

  • Grasp the foundational ideas of machine learning and its types.

  • Design and structure well-posed learning problems.

  • Implement and analyze the Find-S and Candidate Elimination algorithms.

  • Construct decision trees and evaluate their performance.

  • Develop a solid conceptual framework for understanding more advanced ML models.

Designed for beginners and early-stage learners, this course builds a strong theoretical and practical base for further exploration in machine learning and artificial intelligence. It encourages curiosity, experimentation, and applied understanding through hands-on examples.

Who this course is for:

  • Beginners curious about how machines learn from data
  • Computer science and engineering students looking to enhance their academic knowledge
  • Anyone interested in concept learning, algorithmic logic, and model-building fundamentals

Report abuse