Machine Learning Basics

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.

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.

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.

• 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.