Demand continues to grow for professionals who can apply artificial intelligence and machine learning across the end-to-end workflow —from development and evaluation to deployment and monitoring.

This online program provides a structured pathway for building the technical knowledge and practical skills needed to develop AI and machine learning solutions. You’ll strengthen your foundational knowledge, learn how to select and interpret models, and gain hands-on experience building, training, evaluating, deploying and monitoring machine learning models. The program culminates in an applied industry project that you can feature in your professional portfolio.

Designed for scientists, engineers and analytical professionals, the program is intended for learners who are comfortable with quantitative concepts, regularly work with data and have basic programming experience. Whether you’re preparing to transition into an AI or machine learning role or looking to apply these technologies in your current field, you’ll gain practical, project-based experience addressing real-world challenges.

By the end of the program, you’ll be prepared to develop and deploy a machine learning solution and communicate its performance, limitations and business or organizational value. You’ll walk away with a professional portfolio featuring an applied Riipen industry project, technical documentation and a project demonstration. Upon successful completion, you’ll earn a digital badge that you can share with employers and add to your professional profiles.

Who Should Enroll

This applied program is designed for professionals with existing quantitative, data and programming experience. It 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 like Python.

The program is designed for:

• Data analysts, business intelligence developers and analytics managers seeking to move beyond reporting and descriptive analytics into predictive modeling and AI-supported decision-making
• Early- to mid-career data scientists, data engineers and software developers who want practical experience developing, deploying and monitoring machine learning and AI systems
• Scientists and researchers who want to apply machine learning to research or domain-specific data
• Engineers interested in applying machine learning to simulation, sensor, signal, operational or performance data
• Quantitative professionals in fields such as finance, actuarial science, operations research, supply chain, marketing and product analytics who want to incorporate advanced modeling and generative AI into their work
• Professionals in regulated or high-impact fields, including health care, public health, energy, agriculture and public policy, who need to develop and evaluate AI systems responsibly
• Faculty, postdoctoral researchers and graduate students seeking practical, deployment-focused experience to complement their academic or research training

What You’ll Learn

• Apply concepts from linear algebra, probability and optimization to understand model behavior and evaluate different approaches
• Compare and select classical machine learning, deep learning and large language model–based approaches for a given problem, dataset and set of constraints
• Explain how neural networks—including convolutional neural networks, sequence models and transformers—are trained, evaluated and applied
• Build machine learning models in Python using tools such as scikit-learn, PyTorch and TensorFlow/Keras
• Develop generative AI applications using prompt engineering, retrieval-augmented generation and lightweight fine-tuning
• Write reproducible, version-controlled code
• Deploy a model to a cloud endpoint and apply foundational MLOps practices, including continuous integration and delivery, model registries, monitoring and drift detection
• Communicate trade-offs involving accuracy, latency, cost and fairness to technical and nontechnical stakeholders
• Apply responsible AI practices related to bias detection, explainability, privacy and governance

Skills You Will Gain

• Python programming for machine learning 
• Data cleaning, preparation and feature engineering 
• Statistics, probability and applied linear algebra 
• Supervised and unsupervised learning 
• Algorithm and model selection 
• Model training, tuning, validation and evaluation 
• Neural networks and introductory deep learning 
• Generative AI application development
• Model interpretation and explainable AI
• Cloud-based model deployment
• MLOps and model monitoring
• Responsible AI development and governance
• Technical problem-solving, documentation, collaboration and presentation

Course Format and Pacing
This fully online course is delivered through Canvas, UC Davis CPE’s learning management system and includes Coursera-integrated learning materials and project-based experiences facilitated through Riipen, an experiential learning platform connecting learners with real-world business and organizational challenges. 

Course content includes instructor-led video lectures, guided learning activities, interactive discussions, peer collaboration and hands-on exercises, along with mandatory live workshop sessions with the instructor.   

The program runs approximately four months (15 weeks) and requires about 10-12 hours of work per week, including evening sessions designed for working professionals. Students should be prepared for additional learning times outside of live synchronous sessions for project work and collaboration that may be needed.