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:
What you'll learn
Learn the skills that employers are looking for.
Retain knowledge with real-world projects for your portfolio.
We know you’re busy, so we made it flexible.
Attend Live events combined with asynchrounous work.
Access any course
Gain access to our full library of up to date courses.
Free trial included!
Learn from industry experts
Our expert instructors lead data, engineering, and machine learning teams within the world’s most innovative companies.
Senior Manager at Apple and Instructor at Stanford UniversityLinkedIn →
Julie Kallini (co-instructor)
Software Engineer at MetaLinkedIn →
PhD at Stanford; formerly at Waymo, MicrosoftLinkedIn →
PhD at Stanford, formerly at GoogleLinkedIn →
PhD Scholar at StanfordLinkedIn →
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.
We will help you prepare for interviewing at tech companies, no matter the position.
Real-world projects that teach you industry skills and prepare your portfoilo.
80% average completion rate, compared to 15% industry standard.
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! :)
The track was a much better experience than any of the other online courses I've taken.
The track has helped me learn about the latest methods, and bring tools/implementations to my team.
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.
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.
Is this right for me?
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.
Basic data science with Python (Numpy, Pandas, Pyplot, or similar). Co:rise Python for Machine Learning course or equivalent.
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:
Still not sure?
Get in touch and we'll help you decide.
Continue your learning journey with these elective courses