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Program Overview

The AI/ML Lab for Engineering and Science is a 5-day, live-online certificate program designed for experienced engineers and scientists. This program blends hands-on Python with core machine learning, deep learning (PyTorch), LLMs, and generative AI, and MLOps, enabling participants to design, deploy, and scale real solutions.

Program Details

• Course Code: Not specified
• Start Date: February 7
• Time: Saturday, 8:00am - 5:00pm (PST)
• Duration: 5 Days
• Program Type: Open-Enrollment/Public
• Certificate Type: Certificate
• Format: Live-Online
• CEUs: 4
• PDUs: 40
• Fees: $3,500

Program Experience

In this program, participants will delve into the foundations of machine learning algorithms, gaining a deep understanding of data preprocessing, model selection, and evaluation criteria. Through diverse use cases, the program will guide participants along the entire ML lifecycle, arming them with a potent toolkit to conquer industry challenges with confidence.

Benefits

Participants will advance their existing machine learning skills and be able to:

• Understand industry applications of the end-to-end ML lifecycle
• Complete the foundations of ML with analytical methods and statistical deep dives
• Preprocess data to fit the needs of modern ML algorithms
• Understand the entire ML lifecycle and its applications
• Create robust modeling of supervised and unsupervised algorithms
• Know which algorithm to select for various real-world scenarios
• Approach deep learning with applied knowledge of neural networks
• Leverage deep learning methods using modern tools like PyTorch
• Know the differences in compute when using deep learning
• Apply foundational large language models (LLMs) to current use cases

Topics

The course covers the entire machine learning lifecycle and the toolkit needed to create robust machine learning pipelines:

• Data preprocessing techniques
• Statistical foundations of ML
• Exploratory Data Analysis
• Python best-practices
• Modern ML libraries
• Model selection and hyperparameter tuning
• Supervised ML algorithms
• Unsupervised techniques
• ML pipelines
• Data bias and how to avoid it in modeling
• Dimensionality reduction toolkit
• Deep Learning with neural networks
• PyTorch application of deep learning
• Bias-Variance Trade-off
• Natural Language Processing (NLP)
• NLP and deep learning applied to generative AI (LLMs)
• Leverage deep learning techniques such as FFNNs, RNNs, LSTMs
• The modern ML lifecycle
• MLOps and ML deployment to production

Who Should Attend

This program is designed for experienced professionals with a background in engineering, science, or related fields such as aerospace, chemistry, biology, electronics, finance, communications, or technology. It is ideal for those who want to integrate data science and machine learning into their work. Learners are expected to have a solid understanding of calculus, linear algebra, probability, statistics, and basic programming skills, including Python/R.

Schedule

• Course: AI/ML Lab for Engineering & Science
• Duration: 40 Hours
• Live Online (via Zoom): On the following days: February 7, 14, 21, 28, March 7, 2026, 8:00 AM - 5:00 PM Pacific Time

Instructors

• Nicholas Beaudoin: Machine Learning, Generative AI
• Kevin Coyle: AI & Machine Learning
• Matt Brems: Data Science, Artificial Intelligence

Program Outcomes

Participants will complete hands-on notebooks and a small end-to-end project that applies data science and machine learning methods to an engineering-relevant dataset, leaving with reusable code and a brief write-up they can show their team. The program is a non-credit professional program with a pass/fail final grade, based on participation and satisfactory completion of in-class assignments.