Duration
4 weeksWeekly study
3 hours100% online
How it works
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.
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?
Assistant Professor at Sungkyunkwan University
Who developed the course?
Established
1398Location
Seoul, South KoreaWorld ranking
Top 100Source: QS World University Rankings 2021
Ways to learn | Buy this course | Subscribe & save | Limited access |
---|---|---|---|
Choose the best way to learn for you! | $79/one-off payment | $244.99 for a whole year Automatically renews | Free |
Fulfill your current learning need | Develop skills to further your career | Sample the course materials | |
Access to this course | tick | tick | Access expires 12 Mar 2025 |
Access to 1,000+ courses | cross | tick | cross |
Learn at your own pace | tick | tick | cross |
Discuss your learning in comments | tick | tick | tick |
Tests to check your learning | tick | tick | cross |
Certificate when you're eligible | Printed and digital | Digital only | cross |
Cancel for free anytime |
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.
Learning on FutureLearn
Your learning, your rules
- Courses are split into weeks, activities, and steps to help you keep track of your learning
- 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
- Share ideas with your peers and course educators on every step of the course
- Join the conversation by reading, @ing, liking, bookmarking, and replying to comments from others
Map your progress
- As you work through the course, use notifications and the Progress page to guide your learning
- 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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