This course will investigate data mining and machine learning algorithms in supervised and unsupervised learning. Students will understand how to use Python/ R programming language for performing clustering, classification, and regression analysis. In addition, students will learn the capabilities and operation of many algorithms, including decision trees, k-means, k-nearest neighbors, linear regression, ID3 for Decision Trees, and the Perceptron. "Being CDML certified will help you stand out from the crowd and get your chance to apply for desirable positions."
Prerequisites and course requirements
The main required textbooks for this course are listed below and can be readily accessed using the provided links. In addition, there may be additionally required/recommended readings, supplemental materials, or other resources and websites necessary for lessons; these will be provided for you in the course’s General Information and Forums area and throughout the term via the weekly course Unit areas and the Learning Guides.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R. New York, NY: Springer. Available for download here.
Software Requirements/Installation: This course will use two different software tools. The first is the Python/ R programming language environment, and the second is the basic prop neural network simulator. These tools will be available in the Virtual Computing Lab, or you can install them on your computer.
Prerequisites: Students participating in this course must meet the following conditions:
By the end of this course, students will be able to:
This course will cover the following topics in eight learning sessions, with one Unit per week. The Final Exam will take place during Week/Unit 9:
Note: The student can read more detail in Vietnamese here.
Note: Please apply via Zalo (0934. 029.019) or Email (firstname.lastname@example.org)