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:

• Basic knowledge of Python/R programming language, including variables and data types; functions, loop structure...
• Basic knowledge of Data Structures, Algorithms…
• Have a basic mathematical background in Calculus, Integral, and Differential.

Output standard

By the end of this course, students will be able to:

1. Explain the differences among the three main learning styles: supervised, reinforcement, and unsupervised.
2. Understand the process of developing a machine learning project.
3. Implement simple supervised learning, reinforcement learning, and unsupervised learning examples using Python/ R.
4. Understand a range of machine learning algorithms along with their strengths and weaknesses.
5. Understand the basic operation of machine learning algorithms, including decision trees, neural networks, K nearest neighbors, K means clustering and regression.
6. Be able to apply machine learning algorithms to solve simple problems.

Syllabus

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:

• Week 1: Unit 1 – Introduction to Data Mining and Machine Learning
• Week 2: Unit 2 – Tools and Technologies for Data Mining and Machine Learning
• Week 3: Unit 3 – Regression
• Week 4: Unit 4 – Classification
• Week 5: Unit 5 – Decision Trees
• Week 6: Unit 6 – Artificial Neural Networks – Part 1
• Week 7: Unit 7 – Artificial Neural Networks – Part 2
• Week 8: Unit 8 – Unsupervised Learning – Clustering
• Week 9: Unit 9 – Course Review and Final Exam

Note: The student can read more detail in Vietnamese here.

Enrollment information

1. Deadline: 2022, May 03 - 2022, May 09
2. Cost: FREE for the tutor fee.
3. Other fees: $25 (Including practice platform fee and textbook.)
4. Enrollment time: 2022, May 10
5. Schedule: 07 p.m - 09 p.m on Tuesday, Thursday, and Saturday every week.
6. Platform: Online learning

Note: Please apply via Zalo (0934. 029.019) or Email (info@chatwork.org)