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Machine Learning

Data Mining & Machine Learning (DML)

This course will investigate data mining and machine learning algorithms in supervised and unsupervised learning. Students will understand how to use the 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."

Data Mining & Machine Learning (DML)
  • Price 295 USD
  • Case Type Machine Learning
  • Project Deadline 13/05/2022
  • Country Vietnam

Why Choose Our Services

Quality is always our top concern. Therefore, we are committed that students who take courses at Chatwork will have a great experience:

The Chatwork, an electronic certificate issued, is valid forever and is recognized worldwide.

The exam fee is cheap and suitable for all students.

The flexible method of paying exam preparation fees for each student.

The students will receive many endows when applying from our partners' scholarship programs.

The textbook has got the international standards of Chatwork.

The lecturers, who have to get a Master of Science or P.hD, will teach the classes.

The students can approach and practice their English major.

Prerequisites

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.

 

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

 

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. Implement simple supervised learning, reinforcement learning, and unsupervised learning examples using Python/ R.
  3. Understand a range of machine learning algorithms along with their strengths and weaknesses.
  4. Understand the basic operation of machine learning algorithms, including decision trees, neural networks, K nearest neighbors, K means clustering and regression.
  5. Be able to apply machine learning algorithms to solve simple problems.
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