Machine Learning: Regression
ABOUT THE COURSE!
Regression is a large subset of Machine Learning problem that involves predicting a numerical value using known variables without the needs to personally work out relationships between those. While this course’s main focus is on constructing and utilizing an appropriate Regression model on a determined problem, there is also an additional concern that you must understand specific concepts that are universal to Machine Learning as a whole. Therefore, you are highly encouraged to not only perform at the minimum effort and invest in understanding the lessons – the knowledge will come in handy in later courses and are easier to learn here with simpler systems.
This second course of the Machine Learning Program aims at providing learners with interesting topics of Machine Learning including the problems’ concepts, solutions, intuitive reasoning of those solutions, drawbacks, tradeoffs, and more. Particularly, this course aimed to provide you with not only the techniques that bring the best results, but other technical knowledge that will serve you well the longer you push into the realm of Machine Learning. Aside from an indepth experience with dealing with Regression problems, you will learn to assert performance through metrics, the balance between biasvariance, how to apply various penalties to accomplish different tasks, optimizing the models using closedform solutions and gradient descent, and how to present your result with aesthetically pleasing 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 handson exercises.
COURSE INFORMATION
Course code:  MLP302x 
Course name:  Machine Learning: Regression 
Credits:  3 
Estimated Time:  6 weeks. Student should allocate at average of 2 hours/a day to complete the course. 
COURSE OBJECTIVES
After taking this course, the students should all be able to:
 Understand regression problems in machine learning
 Understand and practice Simple Linear Regression
 Understand and practice Multiple Regression
 Understand metrics, why and how are they used for Assessing Performance
 Understand what is Overfit, why it happens and how it impact model quality
 Understand and practice Ridge Regression to resolve Overfit
 Understand and practice LASSO Regression to resolve Overfit
 Understand and practice KNearest Neighbor and Kernel Regression
 Capable of applying all learned techniques to solve realworld problems
COURSE STRUCTURE
Module 1  Regression Overview
 Lesson 1  Understand regression problems
Module 2  Regression Algorithms
 Lesson 2  Regression Fundamentals
 Lesson 3  Optimization Objective
 Lesson 4  Examine the Linear Regression Model
 Lesson 5  Multiple Regression Algorithm
 Lesson 6  Multiple Regression Implementation
 Lesson 7  Performance Assessment Metric
 Lesson 8  Error, Bias and Varriance
Assignment 1  Project  Predict Weather Attributes
Module 3  Regression Tuning
 Lesson 9  The Overfit Problem
 Lesson 10  Using Ridge Regression
 Lesson 11  Using LASSO Regression
 Lesson 12  Implementing LASSO
 Lesson 13  Nearest Neighbor and Kernel
 Lesson 14  Final Evaluation
Assignment 2  Project  Predict Facebook Comment Counts
DEVELOPMENT TEAM
COURSE DESIGNERS
Ph.D. Nguyen Van Vinh 

B.A. Nguyen Hoang Quan 

REVIEWERS & TESTER
Course Reviewer 
Course Tester 

Ph.D. Tran Tuan Anh 

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 
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 program@funix.edu.vn