Introduction to Machine Learning
Introduction to Machine Learning
Title: Introduction to Machine Learning
Author for citation: Anton Boitsev, Aleksei Romanov, Dmitry Volchek, Elena Mikhailova, Natalia Grafeeva, Olga Egorova
License for content: Unknown
Publication date: 2021
This is an ITMO University-created course that is released on the edX platform. The introductory five-week course will have you "learn modern methods of machine learning to help you choose the right methods to analyze your data and interpret the results correctly." The course is free to take. A verified certificate of completion, via a Verified track from ITMO, is available afterwards for $149 USD. (Note that the Audit track expires November 1, 2021.)
The edX course description:
"This course is an introduction to machine learning. It will cover the modern methods of statistics and machine learning as well as mathematical prerequisites for them. We will discuss the methods used in classification and clustering problems. You will learn different regression methods. Various examples and different software applications are considered in the course. You will get not only the theoretical prerequisites, but also practical hints on how to work with your data in MS Azure."
"What you'll learn:
- Introduction to machine learning and mathematical prerequisites
- Regression types (linear, polynomial, multi variable regression)
- Classification methods: Logistic regression, Naïve Bayes and K-nearest neighbors
- Clustering methods: hierarchical and k-means clustering"
About the authors
The course is taught by six instructors. To learn more about the instructors, click on each of their profiles in the course description.
General layout and contents of the course
Week one introduces the learner to machine learning and its mathematical prerequisites that will be required going forward. Week two delves into various aspects of the regression problem, described as "one of the main problems in supervised learning." Week three further looks at regression, specifically logistic regression and why it's not a true regression but a classification problem. Week four continues with classification, with a focus on Naïve Bayes and K-nearest neighbors. The course wraps in week five addressing the clusterization problem and its associated methods.
The course
: The course can be found on the edX site, under the Data Analysis & Statistics category. The session started in September 2021. Enrollment for the free Audit track ends November 1.
