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Final project: Machine Learning Application Development

Enrollment in this course is by invitation only


You have completed four courses in the Machine Learning Program. Having started with the Introduction to Machine Learning course, you have learned about Machine Learning concepts, methodology, algorithms, and applications of Machine Learning. In the second course, Regression, you have implemented regression techniques from scratch and used them to solve regression problems. Then, in the Classification course, you have acquired knowledge on widely used algorithms like Decision Tree, Random Forest, SVM or Neural Network and their application in practical datasets. Also, in the fourth course, you have chance to apply clustering algorithms in solving real-world problems.

Hope you have enjoyed learning these four courses and find yourself ready for this final project. You will start with two projects focusing on building machine learning models to detecting credit card fraud and recognizing intents of user inputs in chatbot systems. In the final task, you will have a chance to engage in a real-world project and apply what you have learned about in previous courses to build your own machine learning project.


Course code: MLP305x
Course name: Final project: Machine Learning
Credits: 3
Estimated Time: 6 weeks. Student should allocate at average of 2 hours/a day to complete the course.


After taking this course, the students should all be able to:

  • Understand machine learning topics: supervised learning, unsupervised learning
  • Understand and use regression algorithms to solve real world problems
  • Understand and use classification algorithms to solve real world problems
  • Understand and use clustering algorithms to solve real world problem



  • Assignment 1: Intent Recognition In Chatbot System
  • Assignment 2: Credit Card Fraud Detection

Final Project  

  • Guide 1: Project Overview
  • Guide 2: Project Details          
  • Guide 3: Project Instruction       
  • Guide 4: Project Rubrics         
  • Guide 5: Project Schedule Guide
  • Guide 6: Project Submission & Defense



Ph.D. Nguyen Van Vinh

  • Lecturer & core member of AI Lab, University of Technology, Vietnam National University (VNU)
  • AI expert & consultant for DPS, Fsoft
  • Ph.D. in Computer Science, Japan Advanced Institute of Science & Technology
  • Bachelor’s degree in IT, University of Science, VNU

B.A. Luu Truong Sinh


Course Reviewer


Course Tester


Ph.D. Tran Tuan Anh

  • Lecturer at Ho Chi Minh National University - University of Science (HCMUS)
  • Ph.D of Computer Science, Chonam National University, Korea
  • M.Sc. Applied Mathematics, University of Orleans, France
    in AI & machine learning

     M.Sc. Nguyen Hai Nam

 Program Reviewers


 Assoc. Prof. Tu Minh Phuong

Dean of IT Faculty Posts and Telecommunications Institute of Technology (PTIT)

Ph.D. Hoang Anh Minh

R&D Manager, FPT Software Chief Scientist, LA Office

Ph.D. Le Hai Son

      Machine Learning Expert       FPT Technology Innovation

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: CourseratutorialspointedX TrainingUdemyMachine 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 or