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1 course
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Introductory
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Andrew Ng
Gain the knowledge and skills
for an AI career
Gain the
knowledge
and skills
for an AI career
Our Courses
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3 courses
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Introductory
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Andrew Ng, Eddy Shyu, Aarti Bagul, Geoff Ladwig
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Stanford Online
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5 courses
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Intermediate
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Andrew Ng, Kian Katanforoosh, Younes Bensouda Mourri
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4 courses
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Intermediate
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Younes Bensouda Mourri, Łukasz Kaiser, Eddy Shyu
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Hugging Face
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3 courses
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Intermediate
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Pranav Rajpurkar, Amirhossein Kiani, Bora Uyumazturk, Eddy Shyu
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4 courses
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Intermediate
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Laurence Moroney
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4 courses
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Intermediate
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Laurence Moroney
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3 courses
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Intermediate
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Sharon Zhou, Eda Zhou, Eric Zelikman
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4 courses
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Intermediate
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Laurence Moroney, Eddy Shyu
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4 courses
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Advanced
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Andrew Ng, Cristian Bartolomé Arámburu, Robert Crowe, Laurence Moroney
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3 courses
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Advanced
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Antje Barth, Shelbee Eigenbrode, Sireesha Muppala, Chris Fregly
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Amazon Web Services (AWS)
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.
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.
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.
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.