gain the knowledge and skills for an AI career

Gain the knowledge and skills
for an AI career

Gain the
knowledge
and skills
for an AI career

Our Courses

Find your learning pathway

Whether you’re a beginner looking to gain foundational knowledge or an experienced practitioner hoping to stay current with advanced skills, our world-class curriculum and unique teaching methodology will guide you through every stage of your AI journey.

Introductory

Introductory programs can be understood by a high school graduate as they require little to no knowledge of AI concepts.

Prerequisites:
Basic math (linear algebra, statistics)
Some coding experience (Python, R, or similar)

Intermediate

Intermediate programs build on Introductory ones and provide an additional experience of concepts and tools across the subfields of AI.

Prerequisites:
Basic math (linear algebra, statistics)
Some coding experience (Python, R, or similar)

Advanced

Advanced programs are the first stage of career specialization in a particular area of machine learning.

Prerequisites:
Strong familiarity with Introductory and Intermediate program material, especially the Machine Learning and Deep Learning Specializations

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Resources

Learning never ends! Done with our Specializations? Check out these interesting resources beyond our curriculum.

Machine Learning Yearning
by Andrew Ng

This is an introductory book about developing ML algorithms. You will learn to diagnose errors in an ML project, prioritize the most promising directions, work within complex settings like mismatched training/test sets, and know when and how to apply various techniques.

MLOps:
From Model-centric to Data-centric AI
by Andrew Ng

In these slides, Andrew Ng shares the skills he sees as fundamental to the next generation of machine learning practitioners.

AI Notes

This is a series of long-form tutorials that supplement what you learned in the Deep Learning Specialization. With interactive visualizations, these tutorials will help you build intuition about foundational deep learning concepts like initializing neural networks and parameter optimization.