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.
This course is part of the Artificial Intelligence: an Overview Specialization
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About this Course
No prerequisites are required: however, having basic statistical notions may help you better understand some considerations.
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Try Coursera for BusinessWhat you will 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
No prerequisites are required: however, having basic statistical notions may help you better understand some considerations.
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessOffered by
Syllabus - What you will learn from this course
Week 1 - Supervised Learning
Week 2 - Unsupervised Learning
Week 3 - Reinforcement Learning
Reviews
- 5 stars63.63%
- 4 stars22.72%
- 3 stars13.63%
TOP REVIEWS FROM MACHINE LEARNING: AN OVERVIEW
This is an excellent course on Machine learning, very concise and well presented.
About the Artificial Intelligence: an Overview Specialization

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