Machine Learning Basics

Delve into the rapidly evolving world of computer science and artificial intelligence with fundamental machine learning concepts. Hone your digital skills with Sungkyunkwan University.

Duration

4 weeks

Weekly study

3 hours

100% online

How it works

Unlimited subscription

Learn more

Established

1398

Location

Seoul, South Korea

World ranking

Source: QS World University Rankings 2021

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.

  • 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

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