Become a ML Engineer

Gain the skills you need to become an effective machine learning engineer in this 3-course track, with applied machine learning, deep learning, deployment of machine learning projects, and more.

We taught over 5000 learners from these companies:

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Machine Learning Foundations

February 19, 2024
12 weeksPart-time
3 course certificates

What you'll learn

Learn the skills that employers are looking for.
Retain knowledge with real-world projects for your portfolio.

  • Course 1
  • Course 2
  • Course 3

Applied Machine Learning

Created and taught by
Andrew Maas - Instructor photo
Co-founder and CEO of Pointable
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See course details

Part-time schedule

We know you’re busy, so we made it flexible.
Attend Live events combined with asynchrounous work.

Too busy? All events are recorded!

Access any course

Gain access to our full library of up to date courses.
Free trial included!

New in-demand courses added monthly.

Learn from industry experts

Our expert instructors lead data, engineering, and machine learning teams within the world’s most innovative companies.

Andrew Maas

Co-founder and CEO of Pointable

LinkedIn →

Julie Kallini (co-instructor)

PhD at Stanford

LinkedIn →

Kevin Wu

PhD at Stanford; formerly at Waymo, Microsoft

LinkedIn →

Eric Wu

PhD at Stanford, formerly at Google

LinkedIn →

Mike Wu

PhD Scholar at Stanford

LinkedIn →

Peer network

Code reviews and study groups. Share experiences and learn alongside a global network of professionals.

A team of people to support you

We have a system in place to make sure you complete the course, and to help nudge you along the way.

Interview preparation

We will help you prepare for interviewing at tech companies, no matter the position.

Project-based learning

Real-world projects that teach you industry skills and prepare your portfoilo.

Best-in-class outcomes

15-20x engagement compared to async courses

Completion certificates

Each course comes with a certificate that you can add to your resume.

Student success stories

Our students ❤️ our courses.

Taking the Applied Machine Learning course in the ML Foundations track has been an incredible experience.

We not only learned tactical skills to approach building state-of-the-art ML models, but also learned important ideas on how to properly setup ML teams, formulate problems, and think about ethics.

All of this was supplemented with fireside chats with industry ML practitioners and leaders who talked about their experiences building teams and integrating ML into their products. It's been an amazing 4 weeks.

Looking forward to my next CoRise class! :)

Max AllenRisk Engineering @ Ramp

The track was a much better experience than any of the other online courses I've taken.

Scott MillslagleData Engineer at Handshake

The track has helped me learn about the latest methods, and bring tools/implementations to my team.

Maggie LinSenior Software Engineer at Guru

I decided to focus on the CoRise courses in the ML Foundations track because they were directly applicable to my day-to-day work. I would lift code that I had written for the course on Sunday and repurpose it for a project at work on Monday. It has made a huge difference in my job performance.

Emily EkdahlSenior Machine Learning Engineer at Palmetto

This is the best team responsiveness I’ve ever seen from an online course. It absolutely shows how much effort the CoRise team puts in to ensure I'm successful.

Josh FeagansDevOps Engineer at Charles Schwab

Is this right for me?

Who is it for?
  • Software Engineers skilled in programming looking for a career change into machine learning engineering.

  • Early Machine Learning Engineers excited about industry applications of machine learning models.

  • Data Scientists who want to learn about the production ML lifecycle.

Prerequisites / Commitment
  • Basic data science with Python (Numpy, Pandas, Pyplot, or similar). CoRise Intermediate Python for Data Science or similar.

  • Enough statistics and linear algebra to keep pace with guided scikit-learn ML modeling. At minimum, some experience in statistics with random variables and linear algebra

No prerequisites? No problem!

You can learn the foundational skills through our courses:

Python Crash Course

Lecturer, UC Berkeley
University of Washington

Intro to Numpy and Pandas

UC Berkeley Tech Policy Fellow & AI Fellow, National Institute of Standards and Technology (NIST) &

SQL Crash Course

Associate Director, Business Analytics

Still not sure?

Get in touch and we'll help you decide.


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