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
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.
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.
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.
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.
Can explain what machine learning is
Can explain the three types of ML
Can explain how ML learns
Can explain the k-NN algorithm
Can explain variations of k-NN
Can explain distance measures
Can explain the linear regression
Can explain Another notation of LR
Can explain additive linear model
Can explain about logistic regression
Can implement logistic regression with scikit learn library
Can evaluate models using confusion matrix
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