Introduction to Machine Learning_Ver1.1
ABOUT THE COURSE!
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 INFORMATION
| 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. |
COURSE OBJECTIVES
- 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
COURSE STRUCTURE
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
DEVELOPMENT TEAM
COURSE DESIGNERS
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Ph.D. Nguyen Van Vinh |
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Ph.D. Tran Hong Viet |
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B.A. Luu Truong Sinh |
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REVIEWERS & TESTER
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Course Reviewer |
Course Tester |
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Ph.D. Tran Tuan Anh |
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M.Sc. Nguyen Hai Nam |
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Program Reviewers
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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:
- Machine Learning Foundations: A Case Study
- Python for Data Science and AI
- Mathematics for Machine Learning
- Basic Statistics
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 or Standford.
Feedback channel
FUNiX is ready to receive and discuss all comments and feedback related to learning materials via email program@funix.edu.vn