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Prof.XINWEI-AI
Major:AI University:XINWEI
หลักการแมชชีนเลิร์นนิง | Fundamentals of Machine Learning
XINWEICourse Descriptions
This course provides a comprehensive introduction to the core concepts, mathematical foundations, and key algorithms of machine learning. You will explore supervised and unsupervised learning techniques, including linear models, decision trees, neural networks, and ensemble methods. Through a blend of theory and practical application, you will learn how to evaluate models, prevent overfitting, and solve real-world data problems. Ideal for beginners, it equips you with the essential skills to build intelligent systems and advance your career in AI.
Total Learning Hours
Total learning hours of 3 hours (Total length of the courses video 3 hours)
Learning Objectives
1. Remember: Define key machine learning terminology and concepts.
2. Understand: Explain the differences between supervised and unsupervised learning algorithms
3. Apply: Implement foundational machine learning models using Python to solve basic data problems.
4. Analyze: Evaluate model performance metrics to diagnose overfitting or underfitting issues.
5. Create: Design and construct an end-to-end machine learning pipeline for a real-world dataset
Target Learners
For anyone who is interested.
Estimate amount of learners 10,000 people
Evaluation
Final Exam 100% graded
Learners requires no less than 70% in order to pass the course and eligible to receive the certificate
MOOC Course Instructors
Main Instructor
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Course Instructor Contact
e-mail:wei.zimiao@geelytalent.com.cn Phone:+86 18739958003
Creative commons
“This course is a part of Thai MOOC
Publish under the Creative Commons Attribution-NonCommercial-ShareAlike (CC BY NC SA)”