The fastest way to learn Applied AI.

Compress 6 months of learning and practice into 2 weeks.
250+ top engineers have completed our hands-on curriculum.

Alumni Spotlight

The AI/ML Roadmap

Foundations

Linear algebra, calculus, stats

Shallow Learning

Classic machine learning

Deep Learning

Artificial neural networks

Pretrained Models

Fine-tuning, transfer learning

AI Engineering

RAG, agents, evals

Superb peers and curriculum

Our program isn't just about adding chatbots. It's about mastering the fundamentals to see possibilities others miss.

You'll learn the building blocks of everything from classic ML to cutting-edge agentic systems, developing the depth to design, build, and ship AI products. This is the foundation for the next decade of your career.

Once accepted, you'll accelerate through our hands-on curriculum, working alongside a select group of peers. No need to piece together a self-study plan — we've mapped the fastest route to frontier AI skills.

Excellent
★★★★★
65+ reviews onTrustpilot

Launch into AI orbit

Our program compresses months of deep study into weeks of intensive, cohort-based learning, designed for those who like to push hard. If you value time, nothing beats learning with us.

Key features:
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Complete project-based, industry-aligned curriculum delivered by world-class instructors
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Get hands-on ML experience, from data cleaning and feature engineering to training and evaluation
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Learn LLM implementation, from context engineering and RAG to agentic workflows and patterns
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Build a foundation in deep learning by experimenting with various neural network architectures
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Join an exclusive professional network of 250+ engineers with weekly meetups and a private job board
Learning in community

Learn in community

The class consists of pair programming sessions, live lectures, group discussions, guest speakers, and more. Lectures are led in a didactic format where student engagement is not just encouraged, but necessary. During class hours, a help desk provides on-demand support if you get stuck.

Due to the intensity of the course, we require you to be fully present during class hours, including taking the necessary time off work.

After graduation, you will be granted membership into our alumni community where teams are formed to build technical portfolio projects.

Trusted by top engineers

Our alumni work at the most innovative companies in the world. They are the architects of the future, building the next generation of AI-powered products and services.

Pick your learning path

Choose the modality that fits your learning style and schedule.

Full-time

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    2 weeks, full-time
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    Live
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    On-demand
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    Nov 10 - Nov 21 (SF)
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    In-Person (SF or NYC)
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    $6,800

Part-time

  • check100hr+ of lectures and projects
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    4 weeks, part-time
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    Live
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    On-demand
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    Aug 29 - Sep 20
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    Remote (Online)
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    $6,800

Success Stories

Our alumni switch to AI teams, start companies, and land jobs at AI companies.

Sandeep Chayapathi

Sandeep Chayapathi

Sandeep was a VP of Engineering at Bulletin before Deep Atlas. Now he's a Tech Lead at Vendelux contributing directly to AI/ML features.

Vijay Pandiarajan

Vijay Pandiarajan

Vijay is a VP of Product at Salesforce. After Deep Atlas, he was promoted to run a new team focused on agentic AI orchestration.

Morgan Wildermuth

Morgan Wildermuth

Morgan is a Tech Lead at Google. After Deep Atlas, she switched to the AI Governance & Tooling team where she works on AI tools.

Blake Danson

Blake Danson

Blake was an Engineer at Nike. After Deep Atlas, he joined Centric Software as an AI/ML Engineer.

Colin Young

Colin Young

After Deep Atlas, Colin joined Limitless as a Staff Engineer working on wearable AI.

Pavan Ravipati

Pavan Ravipati

Pavan was a Principal Solutions Engineer at Github. After Deep Atlas, he joined Cursor's Enterprise GTM team.

Nick Akey

Nick Akey

Nick's ML project (signyourname.io) went viral and then he joined Trunk Tools, an AI-first software startup.

Andrew Han

Andrew Han

After Deep Atlas, Andrew joined Meta as a Tech Lead working on Agentic AI systems.

Shalin Mantri

Shalin Mantri

Shalin was a Director of Product at Google before Deep Atlas. Now he's quietly founding an AI startup.

Frequently Asked Questions

Who is the program designed for?

What is the time commitment?

What are the outcomes from the program?

Why is the program worth it?

Will my employer pay the tuition?

What if I have some ML experience?

Why do I have to apply?

Ready to go deep?

Our application process is designed to help you discover if our program is a good fit. Apply now to start your journey.

See if you qualify →