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.
Machine Learning: an overview
This course is part of Artificial Intelligence: an Overview Specialization
Instructor: Marcello Restelli
Sponsored by PTT Global Chemical
5,454 already enrolled
(133 reviews)
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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
Skills you'll gain
- Dimensionality Reduction
- Artificial Intelligence and Machine Learning (AI/ML)
- Machine Learning Methods
- Data Science
- Data Analysis
- Supervised Learning
- Unsupervised Learning
- Statistical Machine Learning
- Machine Learning
- Applied Machine Learning
- Computer Science
- Machine Learning Algorithms
- Artificial Intelligence
- Reinforcement Learning
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There are 3 modules in this course
What's included
4 videos1 assignment
What's included
3 videos1 assignment
What's included
3 videos1 assignment
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Reviewed on Aug 7, 2023
condensed, good if you (still?) have good math background or spend some time on (re)education after each session
Reviewed on Sep 8, 2023
VERY WELL PREPARED,PRESENTED AND INFORMATIVE COURSE PROVIDED BY COURSERA.
Reviewed on Dec 5, 2022
This is an excellent course on Machine learning, very concise and well presented.
Recommended if you're interested in Information Technology
Johns Hopkins University
University of California San Diego
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