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Machine Learning Basics

Delve into the rapidly evolving world of computer science and artificial intelligence with fundamental machine learning concepts.

380 enrolled on this course

Machine Learning Basics

380 enrolled on this course

  • 4 weeks

  • 3 hours per week

  • Digital certificate when eligible

  • Introductory level

Find out more about how to join this course

  • Duration

    4 weeks
  • Weekly study

    3 hours
  • 100% online

    How it works
  • Unlimited subscription

    $244.99 for a whole yearLearn more

Sharpen your digital skills by leveraging predictive analytics

From the healthcare industry to the financial sector to retail operations, the world is experiencing a gold rush of technological innovation, with machine learning at its forefront.

As the demand for skilled machine learning engineers continues to skyrocket, keep pace with the competition by joining this flexible, four-week course from Sungkyunkwan University.

Hone highly sought-after AI skills and grasp machine learning’s most fundamental concepts to become a tech-savvy player in the digital age.

Grasp the basics of data-driven machine learning

As one of the hottest topics today, machine learning has found its way into everyday vernacular. But what exactly is it?

You’ll begin this course by answering just that, exploring the ways it relies on data to identify patterns, make predictions, and automate decision-making processes.

At this point, you’ll also explore the different machine learning models (supervised learning, unsupervised learning, and reinforcement learning) and how these models operate.

Understand the concept of k-Nearest Neighbours (kNN) algorithm

Known for its simplicity and effectiveness, you’ll then explore the k-Nearest Neighbours (kNN), a machine learning algorithm. After studying its core concepts, you’ll dive into its variations and diverse applications, including distance measures.

Apply linear regression and logistic regression as machine learning tools

Next, you’ll learn how to model relationships between variables using linear regression for continuous outcomes and how to classify binary outcomes with logistic regression.

By the course’s end, you’ll possess essential AI skills and a strong grasp of machine learning fundamentals ensuring you stay competitive in today’s digital landscape.

Syllabus

  • Week 1

    The basic concepts of machine learning

    • What is machine learning

      Can explain what machine learning is

    • Three types of machine learning

      Can explain the three types of ML

    • Model Analysis

      Can explain how ML learns

  • Week 2

    The k-Nearest Neighbors

    • The basic concept of kNN

      Can explain the k-NN algorithm

    • Variation of k-NN

      Can explain variations of k-NN

    • Examples with kNN

      Can explain distance measures

  • Week 3

    Linear Regression

    • Linear regression I

      Can explain the linear regression

    • Linear regression II

      Can explain Another notation of LR

    • Linear regression III

      Can explain additive linear model

  • Week 4

    Logistic Regression

    • Logistic Regression I

      Can explain about logistic regression

    • Logistic Regression II

      Can implement logistic regression with scikit learn library

    • Logistic Regression III

      Can evaluate models using confusion matrix

When would you like to start?

Start straight away and join a global classroom of learners. If the course hasn’t started yet you’ll see the future date listed below.

  • Available now

Learning on this course

On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.

What will you achieve?

By the end of the course, you‘ll be able to...

  • Understand the fundamental concepts of machine learning, including its types and learning processes.
  • Explain and apply the k-NN algorithm and its variations, including different distance measures.
  • Comprehend linear regression and its alternative notations, along with the additive linear model.
  • Understand logistic regression and implement it using the scikit-learn library.
  • Evaluate machine learning models using tools such as the confusion matrix.

Who is the course for?

This course is for anyone curious about the world of artificial intelligence and interested in learning more about machine learning.

While open to all, it’s recommended you have a working knowledge of Python and related data analysis concepts.

Who will you learn with?

Kim Jaekwang

Assistant Professor at Sungkyunkwan University

Who developed the course?

Sungkyunkwan University logo

Sungkyunkwan University (SKKU)

Sungkyunkwan University, founded in 1398 as the highest national educational institute in the early years of the Joseon Dynasty in Korea, has fostered leaders of Korean society for over 600 years.

  • Established

    1398
  • Location

    Seoul, South Korea
  • World ranking

    Top 100Source: QS World University Rankings 2021

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Ways to learn

Choose the best way to learn for you!

Subscribe & save

$244.99 for a whole year

Automatically renews

Develop skills to further your career

  • Access to this course
  • Access to 1,000+ courses
  • Learn at your own pace
  • Discuss your learning in comments
  • Tests to boost your learning
  • Digital certificate when you're eligible

Cancel for free anytime

Buy this course

$79/one-off payment

Fulfill your current learning need

  • Access to this course
  • Learn at your own pace
  • Discuss your learning in comments
  • Tests to boost your learning
  • Printed and digital certificate when you’re eligible

Limited access

Free

Sample the course materials

  • Access expires 12 Mar 2025

Find out more about certificates, Unlimited or buying a course (Upgrades)

Sale price available until 3 March 2025 at 23:59 (UTC). T&Cs apply.

Find out more about certificates, Unlimited or buying a course (Upgrades)

Sale price available until 3 March 2025 at 23:59 (UTC). T&Cs apply.

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  • Learn through a mix of bite-sized videos, long- and short-form articles, audio, and practical activities
  • Stay motivated by using the Progress page to keep track of your step completion and assessment scores

Join a global classroom

  • Experience the power of social learning, and get inspired by an international network of learners
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Map your progress

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  • Whenever you’re ready, mark each step as complete, you’re in control
  • Complete 90% of course steps and all of the assessments to earn your certificate

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