Machine Learning Algorithms
Master key machine learning algorithms: Naïve Bayes, Support Vector Machines, Decision Trees, and Clustering in this comprehensive course.
Master key machine learning algorithms: Naïve Bayes, Support Vector Machines, Decision Trees, and Clustering in this comprehensive course.
This course provides a comprehensive introduction to fundamental machine learning algorithms. Students will gain a deep understanding of Naïve Bayesian, Support Vector Machine, Decision Tree algorithms, and Clustering techniques. The curriculum covers theoretical foundations and practical applications of these algorithms, including probability concepts, kernel methods, regression trees, random forests, and Gaussian mixture models. Through a combination of video lectures, readings, and quizzes, learners will develop a solid grasp of how these algorithms work and when to apply them. The course assumes basic knowledge of Python programming and mathematics, including matrix multiplications and conditional probability. By the end, students will be equipped with essential skills to implement and utilize these core machine learning algorithms in real-world scenarios.
Instructors:
English
Powered by
What you'll learn
Understand and implement the Naïve Bayesian algorithm
Master the concepts and application of Support Vector Machines
Explore Decision Tree algorithms, including regression trees and random forests
Learn various Clustering techniques, including k-means and Gaussian mixture models
Apply probability and conditional probability concepts in machine learning
Understand kernel methods and their use in Support Vector Machines
Skills you'll gain
This course includes:
15 Hours PreRecorded video
13 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
Created by
Provided by
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 4 modules in this course
This course offers a comprehensive exploration of key machine learning algorithms, focusing on Naïve Bayesian, Support Vector Machine, Decision Tree, and Clustering techniques. The curriculum is structured to provide both theoretical understanding and practical application skills. Each module delves into the fundamental concepts, mathematical foundations, and implementation strategies of these algorithms. Students will learn about probability and conditional probability, Bayesian reasoning, kernel methods, regression trees, random forests, and various clustering approaches including k-means and Gaussian mixture models. The course emphasizes the importance of understanding when and how to apply each algorithm, along with their strengths and limitations. Through a combination of video lectures, in-depth readings, and interactive quizzes, students will develop a robust understanding of these core machine learning techniques, preparing them for advanced applications in data science and artificial intelligence.
Naïve Bayes
Module 1 · 3 Hours to complete
Support Vector Machine
Module 2 · 3 Hours to complete
Decision Tree
Module 3 · 3 Hours to complete
Clustering
Module 4 · 4 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Assistant Professor at Sungkyunkwan University and Advocate for Faith and Student Support
Jaekwang Kim is an Assistant Professor at Sungkyunkwan University, affiliated with the School of Convergence, the Department of Computing, and the Department of Applied Data Science. He earned his B.S., M.S., and Ph.D. degrees from Sungkyunkwan University in 2004, 2006, and 2014, respectively. Dr. Kim's research focuses on artificial intelligence, particularly in recommendation algorithms and intelligent systems. He is also actively involved in campus ministry as a member of the University Bible Fellowship, supporting students in both their faith and academic pursuits.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.