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.
Course taught by expert instructors
Senior Manager at Apple and Instructor at Stanford University
Andrew Maas is co-founder and CEO of Pointable, a platform for metrics-driven development of RAG-LLM conversational agents. He previously led teams developing data-centric deep learning approaches at Apple and was a co-founder of Roam Analytics (acquired by Parexel) -- a natural language extraction platform for healthcare. Andrew earned a PhD in computer science from Stanford University, advised by Andrew Ng and Dan Jurafsky, where his work focused on large-scale deep learning for spoken and written language. Andrew also advises machine learning startups and teaches a graduate course on spoken language processing at Stanford.
PhD at Stanford
Julie Kallini is a computer science PhD student at Stanford University, advised by Chris Potts and Dan Jurafsky. She is a member of the Stanford Natural Language Processing (NLP) group, and her research broadly spans topics in NLP, machine learning interpretability, and computational linguistics. Previously, Julie was a software engineer at Meta, where she applied machine learning and content understanding techniques to privacy problems in advertisements. Before joining Meta, Julie graduated summa cum laude from Princeton University with a B.S.E. in computer science.
Learn and apply skills with real-world projects.
Anyone involved in machine learning projects seeking to design, plan, and execute projects more effectively
People looking for a technical introduction to applying ML techniques in an industry setting
Software engineers transitioning to machine learning engineering projects
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
Try these prep courses first
Initial ML problem formulation and basic introduction to regression models
- ML models and evaluation metrics for regression
- Basic formulation of supervised ML tasks and modeling assumptions
- Formulate and build an ML system for a business challenge
Initial classification models and evaluation metrics on multiple datasets.
- Models to generate binary, multi-class, and other classification outputs
- Evaluation metrics and diagnostics for classification modeling tasks
- ML problem formulation and project planning steps
Competitive results on one of several benchmark ML tasks we provide
- State of the art modeling techniques and best practices to achieve good results on new datasets
- Methods to preprocess and featurize common data types (text, images, audio, tabular)
- Introductory data-centric ML techniques to improve performance of a final model
Leverage deep learning foundation models for audio classification
- Foundation model features and adversarial inputs
- Ethics and bias considerations in ML projects
- Where to go next? How to continue building upon this class in industry or further studies
A course you'll actually complete. AI-powered learning that drives results.
Transform your learning programs with personalized learning. Real-time feedback, hints at just the right moment, and the support for learners when they need it, driving 15x engagement.
Live courses by leading experts
Our instructors are renowned experts in AI, data, engineering, product, and business. Deep dive through always-current live sessions and round-the-clock support.
Practice on the cutting edge
Accelerate your learning with projects that mirror the work done at industry-leading tech companies. Put your skills to the test and start applying them today.
Flexible schedule for busy professionals
We know you’re busy, so we made it flexible. Attend live events or review the materials at your own pace. Our course team and global community will support you every step of the way.
Each course comes with a certificate for learners to add to their resume.
15-20x engagement compared to async courses
Support & accountability
You are never alone, we provide support throughout the course.
Get reimbursed by your company
More than half of learners get their Courses and Memberships reimbursed by their company.
Hundreds of companies have dedicated L&D and education budgets that have covered the costs.
Course success stories
Learn together and share experiences with other industry professionals
Co:rise is young but already in a league of its own when compared to other online upskill/career changing courses. Courses are intimate and students are driven. It felt more like an accelerated university level course than on online certificate program in that I learned more in 4 weeks that I have with any other online course. The most unique part is that you get facetime with instructors and mentors who have proven track records in the domain they are teaching.
This course gave me the boost I needed in my day job to keep up with ML topics and my ML engineer colleagues. The pace and time needed for the course fit nicely into my busy schedule while also giving me tangible skills quickly. Real-time support from the Uplimit team was a key part of my success. I highly recommend this course!
Taking the Applied Machine Learning course 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 :)
I was so excited about this class, that I dropped my grad class that I was taking at the same time. One of the things that got me really excited is Andrew’s years in the field allowed him to take complicated concepts and simplify them. When Andrew talked about problem formulation, and running a smaller experiment, it was pivotal in giving me the confidence at work to propose a smaller solution, publish early, talk about our methods. Mentorship with the course team was AMAZING.
You can learn a lot in this course even if you don’t have much prior Python or ML experience as long as you are willing to put in some time on the projects
A crash course in learning the basics to get you started on building your first ML models while using state of the art techniques to further improve performance.
I would definitely recommend the Uplimit community as it provides a major incentive and community compared to most MOOCs - It's what you make of it, but if you're invested and put a lot in, you will get even more out
Started as a novice to machine learning I get to learn so much in just 4 weeks of course. Starting from basics to training state of the art ML models. The way projects are designed is exceptional and is very helpful in learning quick. Team is helpful and very prompt in responding to any queries.