Machine Learning for Data Science
ABOUT THE COURSE!
As part of our Certificate Program in Data Science, this third course of the Data Science Program aims at providing a fundamental understanding of machine learning and its applications in different practical fields.
Machine Learning is believed to be the science of enabling computers to act without being directly programmed. For the last ten years, different applications of Machine Learning like speech recognizing systems, self-driving cars or smart web search engines have posed tremendous impacts in our daily life activities.
This third course of the Data Science Program aims at providing a fundamental understanding of machine learning and its applications in different fields such as health care, banking, telecommunication and so forth. Learners will have a chance to explore the most effective machine learning techniques as well as putting them into practice for implementation in their own business. More importantly, these theoretical foundations of learning and practical know-how are essential for handling new problems promptly and effectively.
To begin the course, let's take a few minutes to explore the course site. Review the material we’ll cover each week, and preview the assignments/projects/quizzes you’ll need to complete to pass the course.
Main concepts are delivered through videos, demos and hands-on exercises.
COURSE INFORMATION
| Course code: | DSP303x |
| Course name: | Machine Learning |
| Credits: | 3 |
| Estimated Time: | 6 weeks. Student should allocate at average of 2 hours/ day to complete the course. |
COURSE OBJECTIVES
After taking this course, the students should all be able to:- Get an overview of the definitions and different topics in Machine Learning: supervised learning, unsupervised learning.
- Acquire overview and basic knowledge about Regression.
- Be able to apply Regression in solving real problems.
- Acquire overview and basic knowledge about Classification.
- Be able to apply Classification in solving real problems.
- Acquire overview and basic knowledge about Clustering.
- Be able to apply Clustering in solving real problems.
- Acquire overview and basic knowledge about Recommender System.
- Be able to apply Recommender System in solving real problems.
- Proficiently manipulate typical and basic libraries in machine learning with Python: Numpy, sklearn, pandas, matplotlib.
- Comprehend evaluation methods and metrics in different problems of machine learning.
- Be able to apply Ensemble Learning in boosting model accuracy
COURSE STRUCTURE
Module 1 - What is Machine Learning?
- Lesson 1 - Introduction to Machine Learning
Module 2 - Regression
- Lesson 2 - Linear regression
- Lesson 3 - Advanced Regression
Assignment 1 - Project - Regression
Module 3 - Classification
- Lesson 4 - Classification
- Lesson 5 - Logistic Regression
- Lesson 6 - Decision Trees
- Lesson 7 - Support Vector Machine
Assignment 2 - Project - Classification
Module 4 - Clustering
- Lesson 8 - K-Means Clustering
- Lesson 9 - Hierarchical Clustering
- Lesson 10 - DBSCAN
Assignment 3 - Project - K-Means
Module 5 - Recommendation system
- Lesson 11 - Content-based Recommendation Systems
- Lesson 12 - Collaborative Filtering
DEVELOPMENT TEAM
COURSE DESIGNERS
M.Sc. Nguyen Hai Nam
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M.S. Vu Thuong Huyen
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REVIEWERS
Course Reviewer
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Ph.D. Dang Hoang Vu |
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Assoc. Prof. Tu Minh Phuong |
Ph.D. Nguyen Van Vinh |
Ph.D. Tran The Trung |
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MOOC MATERIALS
Below is the list of all free massive open online learning sources (MOOC) from Coursera used for this course by FUNiX:
Learning resources
In modern times, each subject has numerous relevant studying materials including printed and online books. FUNiX Way does not provide a specific learning resource but offers recommendation for students to choose the most appropriate source to them. In the process of studying from many different sources based on that personal choice, students will be timely connected to a mentor to respond to their questions. All the assessments including multiple choice questions, exercises, projects and oral exams are designed, developed and conducted by FUNiX.
Learners are under no obligation to choose a fixed learning material. They are encouraged to actively find and study from any appropriate sources including printed textbooks, MOOCs or websites. Students are on their own responsibilities in using these learning sources and ensuring full compliance with the source owners’ policies; except for the case in which they have an official cooperation with FUNiX. For further support, feel free to contact FUNiX Academic Department for detailed instructions.
Learning resources are recommended below. It should be noted that listing these learning sources does not necessarily imply that FUNiX has an official partnership with the source’s owner: Coursera, tutorialspoint, edX Training, Udemy, Machine Learning cơ bản, or Towards Data Science.
Feedback channel
FUNiX is ready to receive and discuss all comments and feedback related to learning materials via email [email protected]