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Introduction to Machine Learning

Enrollment in this course is by invitation only


Machine learning is the application of artificial intelligence (AI) that provides machines with the ability to automatically learn and improve without being explicitly programmed for the task. The main focus of Machine Learning is to provide algorithms to build and train such systems so that they can solve determined problems. Therefore, it is very important to understand what is machine learning and how to apply it on your work.  

This first course of Introduction to Machine Learning aims at providing learners with an overview of Machine learning and its related subjects with application in real world. Particularly, learners will be equipped with Linear Algebra, descriptive statistics and probability which are necessary for machine learning. They will have a chance to explore Python for machine learning, an approachable and well-known programming language. More importantly, through a series of practical case studies, learners will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, Information Retrieval and Deep Learning with Searching for Images. 

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 code: MLP301x
Course name: Introduction to Machine Learning
Credits: 3
Estimated Time: 6 weeks. Student should allocate at average of 2 hours/a day to complete the course.


  • Understand the basics of Machine Learning concept
  • Understand the basic concepts of  Linear Algebra, descriptive statistics and probability
  • Comprehend and practice basic Python programming, data structures in Python, working with Pandas and Numpy, Classes and Inheritance
  • Comprehend and Practice with tool for Machine Learning
  • Outline the basics of Supervised and Unsupervised Learning in Machine Learning with case studies


Module 1 - Machine Learning Overview

  • Lesson 1: Welcome to Machine Learning

Module 2 - Python for Machine Learning

  • Lesson 2: Python Basics with Data Structures
  • Lesson 3: Python Advance with OOP and API
  • Lesson 4: Numpy in Python
  • Lesson 5: Working with Data and Pandas
  • Lesson 6: Data Visualization with Matplotlib

Assignment 1: Project - Test Grade Calculator

Module 3 - Mathematics for Machine Learning

  • Lesson 7: Linear Algebra - Vectors
  • Lesson 8:  Linear Algebra - Matrices
  • Lesson 9: Multivariate Calculus - Gradient and Derivatives
  • Lesson 10: Multivariate Calculus - Chain Rule and Optimization
  • Lesson 11: Descriptive Statistics
  • Lesson 12: Correlation and Regression
  • Lesson 13: Probability
  • Lesson 14: Probability Distributions

Progress Test

Module 3 - Machine Learning Foundations: A Case Study

  • Lesson 15: Linear Regression Problem
  • Lesson 16: Linear Regression Case Study
  • Lesson 17: Classification Problem
  • Lesson 18:  Classification Case Study
  • Lesson 19: Clustering Problem
  • Lesson 20: Clustering Problem
  • Lesson 21: Recommender System
  • Lesson 22: Recommender System Case Study
  • Lesson 23: Deep Learning Problem
  • Lesson 24: Deep Learning Case Study

Assignment 2: Project - Sentiment analysis and image classification example 



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

Ph.D. Tran Hong Viet

  • Ph.D. in Computer Science, Teacher in Faculty of Information Technology, University of Economics and Technical Industries since 2002
  • Master degree in Hanoi University of Science and Technology in 2006
  • Ph.D. dissertation defended in VNU University of Engineering and Technology in 2019

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



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: CourseratutorialspointedX TrainingUdemy or Standford.

 Feedback channel

FUNiX is ready to receive and discuss all comments and feedback related to learning materials via email