Developing effective AI and machine learning solutions requires more than training a model. You’ll learn to select appropriate approaches, evaluate performance and limitations and communicate a solution’s business or organizational value. Through applied learning, you’ll develop the technical judgment to make informed decisions across the machine learning lifecycle.
Skills You'll Gain
- Python programming for machine learning
- Model selection, training and evaluation
- Neural networks and deep learning
- Generative AI application development
- Cloud deployment, MLOps and monitoring
- Explainable and responsible AI
- Technical documentation and presentation
Take a Machine Learning Solution from Idea to Deployment
Apply your learning to a real business or organizational challenge through a project facilitated by Riipen.
Prepare data, select an approach and train a machine learning solution using industry-relevant tools.
Deploy a model to a cloud endpoint and apply foundational MLOps practices to track performance and drift.
Communicate performance, limitations and organizational value through documentation and a project demonstration.
Who is this Program For?
Designed for professionals who already work with quantitative concepts, data and basic code and want applied, deployment-focused experience.
- Data analysts, business intelligence developers and analytics managers ready to move into predictive modeling
- Data scientists, data engineers and software developers seeking end-to-end AI and ML experience
- Scientists, researchers and engineers applying machine learning to domain-specific data
- Quantitative professionals in finance, operations, supply chain, marketing and product analytics
- Professionals in health care, energy, agriculture, public health and public policy who need responsible AI practices
Faculty, postdoctoral researchers and graduate students who want applied technical experience
Make sure the program matches your background
This is not an introduction to programming or data analysis. You do not need to be a data scientist, but you should be comfortable with linear algebra and statistics, have experience working with data and be able to write and debug basic code in a language such as Python.
A Structured Online Experience
The format combines flexible learning with required interaction and project work so you can build technical skills with guidance and accountability.
Learn through Canvas and Coursera
Access instructor-led video lectures, guided activities, hands-on exercises and integrated learning materials in a structured online environment.
Join Live Workshops
Work with the instructor and your peers through interactive discussions, evening workshop sessions and collaborative learning.
Work on a Real-World Project
Use Riipen to connect your learning with a real business or organizational challenge and produce portfolio-ready deliverables.
Leave with proof of what you can do
By the end of the program, you will be prepared to develop and deploy a machine learning solution and explain how it performs, where it is limited and how it can create value.
- An applied industry project
- Technical documentation
- A project demonstration
- UC Davis CPE digital badge
Get Started with AI and Machine Learning
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