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URL: https://www.coursera.org/learn/machine-learning-overview

⇱ Machine Learning: an overview | Coursera

Machine Learning: an overview

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Machine Learning: an overview

11,211 already enrolled

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Gain insight into a topic and learn the fundamentals.
4.6

219 reviews

Beginner level

Recommended experience

3 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
4.6

219 reviews

Beginner level

Recommended experience

3 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Classify machine learning problems, supervised learning problems and describe the limitations of machine learning techniques in supervised learning

  • Classify machine learning problems in unsupervised learning, describe the utility of dimensionality reduction techniques

  • Formulate a sequential decision-making problem, explain what a value function is and describe how to optimize a policy in reinforcement learning

Details to know

Shareable certificate

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Assessments

3 assignments

Taught in English

Build your subject-matter expertise

This course is part of the Artificial Intelligence: an Overview Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 3 modules in this course

The course provides a general overview of the main methods in the machine learning field. Starting from a taxonomy of the different problems that can be solved through machine learning techniques, the course briefly presents some algorithmic solutions, highlighting when they can be successful, but also their limitations. These concepts will be explained through examples and case studies.

What's included

4 videos1 assignment

4 videos•Total 33 minutes
  • Introduction to machine learning •9 minutes
  • Supervised Learning problem•9 minutes
  • Regression and Classification problems•8 minutes
  • Model Selection•7 minutes
1 assignment•Total 30 minutes
  • Quiz•30 minutes

What's included

3 videos1 assignment

3 videos•Total 21 minutes
  • Unsupervised Learning: Clustering•7 minutes
  • Unsupervised Learning: Dimensionality Reduction•7 minutes
  • Unsupervised Learning: Association Rules•7 minutes
1 assignment•Total 30 minutes
  • Quiz•30 minutes

What's included

3 videos1 assignment

3 videos•Total 21 minutes
  • Sequential decision-making problems•7 minutes
  • Markov decision processes•7 minutes
  • Reinforcement Learning algorithms•7 minutes
1 assignment•Total 30 minutes
  • Quiz•30 minutes

Earn a career certificate

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Instructor

Instructor ratings
4.5 (99 ratings)
Politecnico di Milano
1 Course•11,211 learners

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Showing 3 of 219

SC
·

Reviewed on May 31, 2026

Good easy to understand. It’s good where we can learn more things in this course, so it’s improve our knowledge and its improve our technical

RS
·

Reviewed on Sep 8, 2023

VERY WELL PREPARED,PRESENTED AND INFORMATIVE COURSE PROVIDED BY COURSERA.

AM
·

Reviewed on Oct 5, 2025

For everyone i strongly suggest attending it, as the insights provided could be highly beneficial for our work.

Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

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