mooc-course.com is learner-supported. When you buy through links on our site, we may earn an affiliate commission.
Introduction to Machine Learning (edX)
Key Facts
Provider | School | Language | Level |
---|---|---|---|
edX | ITMO University | English | Introductory |
By enrolling in this online course you will spend approx. 5 Weeks/2–4 hours per week to learn key concept of Machine Learning.
Course Overview
Learn the essentials of machine learning and algorithms of statistical data analysis.
Want to learn how to analyze the huge amounts of data? In this course you will learn modern methods of machine learning to help you choose the right methods to analyze your data and interpret the results correctly.
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.
Course Syllabus
Week 1: Introduction to machine learning and mathematical prerequisites. The concepts of machine and statistical learning are introduced. We discuss the main branches of ML such as supervised, unsupervised and reinforcement learning, give specific examples of problems to be solved by the described approaches. Besides, we show that ML is not as powerful as one can think. Finally, we remind you of some basic concepts of mathematics used in further lectures.
Week 2: Regression (linear, polynomial, multivariable regression). Regression problem is one of the main problems in supervised learning. We start with the heuristic approach trying to solve a very practical problem and come to rigorous mathematical construction of the simple linear regression model. We go further and describe statistical properties of the model: confidence intervals for the model’s parameters, hypothesis testing of linear dependence. Finally, we come to a so-called multivariable linear and polynomial regressions and show some examples and applications.
Week 3: Logistic regression. The second branch of supervised learning is a classification problem. We deal with a two-class logistic regression and emphasise that it is not a regression at all. Then why is it called so? It’s construction is closely connected with linear regression described in the 2nd lecture. We remind you a maximum likelihood estimation method and its applications to logistic regression. Finally, we discuss some applications of the logistic regression to a football game predictions and describe ROC analysis or a quality testing approach for the described model.
Week 4: Naïve Bayes and K-nearest neighbors. In this lecture we continue with classification problem. We introduce a so-called naive Bayes approach to classification widely used in e-mail spam recognition until 2010. Then we come to a multi-class classification using K-nearest neighbors method. What are the metrics that we will use? How does a particular metric influence the result? What is K and how do you choose it solving a particular problem? These are the questions that are rigorously discussed in the lecture.
Week 5: Clustering methods: hierarchical and k-means clustering. Clusterization problem is at the heart of unsupervised learning. We have a lot of data and nothing else: we don’t know the amount of classes, similarities in objects, we know almost nothing. We show how to establish some order in the given chaotic data using hierarchical clustering method and k-means approach. How to establish the initial clusters, what metric to choose, what actually means “close and far” objects? These questions are discussed in the lecture.
Meet your Instructors
Olga Egorova
Natalia Grafeeva
Elena Mikhailova
Dmitry Volchek
Anton Boitsev
Aleksei Romanov
* We try to provide you as many free online courses from multiple providers and universities but in some cases you may have pay nominal fee to get assignments and/or to claim verified Certificate or Degree from respective provider.
Similar Machine Learning MOOC Courses
Introduction to Machine Learning (edX) Course Review
Already taken Introduction to Machine Learning (edX) course? Share your review on MOOC course content, Certification, Assignment, Exam to help others.
{{ reviewsLength }} Review
{{ reviewsLength }} Reviews
Introduction to Machine Learning (edX)
- Boosts your Resume and Career.
- Learn online for FREE or at low price*
- Learn new Technology from the worlds best Universities & Teachers.
- Learn as per your convenient time.
- Learn from anywhere from any device.
- Earn a Certificate/Degree on completion of course*
Share this Course