Machine Learning Foundations
3 courses — included as part of your subscription.
Track

Machine Learning Foundations

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.

Applied Machine Learning
Deep Learning Essentials
Data Centric Deep Learning
Real-world projects that teach you industry skills.
Learn alongside a small group of your professional peers
Connect with experts through live sessions and office hours
Price
Included with your subscription
Track Duration
3 courses x 4 weeks each
Start Date
January 2, 2023
Registration By
December 30, 2022
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Course 1
Applied Machine Learning

Design, build, and debug machine learning models for classification and regression tasks using a variety of datasets with Python (Numpy, Scikit, Pyplot). Learn best practices to plan and execute ML development projects whether large or small.

CREATED AND TAUGHT BY:
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Andrew Maas
Senior Manager at Apple and Instructor at Stanford University
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Julie Kallini (co-instructor)
Software Engineer at Meta
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COURSE CURRICULUM:
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Course 2
Deep Learning Essentials

Learn the foundations of deep learning and practice with training and implementing neural networks in Pytorch while covering topics including convolutional neural networks (CNNs), transformers, and generative adversarial networks (GANs).

CREATED AND TAUGHT BY:
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Kevin Wu
PhD at Stanford; formerly at Waymo, Microsoft
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Eric Wu
PhD at Stanford, formerly at Google
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COURSE CURRICULUM:
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Course 3
Data Centric Deep Learning

Learn to build, improve, and repair deep learning models with a data-centric approach. This course will put you in the shoes of a deep learning engineer, and simulate the real world challenge of improving data quality, building and testing deep learning models, and improving performance with a human-in-the-loop. Week by week, we will develop an understanding of the critical role of data in deep learning operations – from integration tests to deep learning tooling to iterative annotation. Learn the best practices for deep learning in the real world.

CREATED AND TAUGHT BY:
Instructor Photo
Andrew Maas
Senior Manager at Apple and Instructor at Stanford
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Mike Wu
PhD Scholar at Stanford
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COURSE CURRICULUM:

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Nihit Desai
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January 9, 2023

Natural Language Processing

Sourabh Bajaj
former Engineering Manager at Neeva
Kaushik Rangadurai
Software Engineer at Meta

Trusted by learners from top 500 companies:

Interested in upskilling your company?

I’ve taken lots of online courses before, so I viewed the track from that lens. The track was a much better experience than any of the other courses.

Scott MillslagleData Engineer at Handshake

The track has helped me to have the confidence to talk about ML with my team, that I’m learning about the latest methods, and bring tools/implementations to my team.

Maggie LinBack End/Machine Learning Engineer at Guru

The thing I’ve been the most impressed is how well the CoRise team has struck a balance of catering to a different range of skill levels, different approaches.

Paige WendlandData-Driven Product Developer

The whole course team is pretty active on Slack, it 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

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 co:rise class :)

Max AllenRisk Engineering at Ramp

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

Real-world projects

Work on projects that bring your learning to life.
Made to be directly applicable in your work.

Live access to experts

Sessions and Q&As with our expert instructors, along with real-world projects.

Network & community

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

Support & accountability

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

All-access to courses and tracks

For $1000, get access to this track ($1500 value) + the complete catalog!
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Average course price $500

This track is 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

Familiarity with basic Python programming, web development, and have completed a first-course in machine learning (or any equivalent)

Having some background in calculus, statistics, and linear algebra is also a plus. However, don't worry if you don't remember everything from that one calculus class you took in high school! We won't assume any specific knowledge, though concepts like derivatives and matrices will be touched on.

Frequently Asked Questions

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