Introduction to Machine Learning

Duke surveys several model families rather than concentrating on one framework feature. Model-learning basics lead into convolutional image analysis, recurrent language networks, transformers and reinforcement learning. The provider describes hands-on PyTorch exercises as part of the course’s modeling approach.

What you will learn

• Compare model families for image and language problems.
• Explain the role of convolutional, recurrent and transformer networks.
• Identify the basic setting of a reinforcement-learning problem.

Who this suits

Intermediate learners; detailed prerequisites are not specified in the published course facts. Preview the programming and mathematics expectations.

Before you enroll

Check the required PyTorch environment and how much coding guidance the exercises provide. A broad introduction may need a focused follow-up before applying one model family to a complex project.

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Put it into practice

Choose one image or text task and compare two possible model families. Explain the data requirements, evaluation method and practical reason for your first choice.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

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