Participants will advance their existing machine learning skills and be able to:
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Understand industry applications of the end-to-end ML lifecycle
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Complete the foundations of ML with analytical methods and statistical deep dives
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Preprocess data to fit the needs of modern ML algorithms
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Understand the entire ML lifecycle and its applications
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Create robust modelling of supervised and unsupervised algorithms
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Know which algorithm to select for various real-world scenarios
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Approach deep learning with applied knowledge of neural networks
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Leverage deep learning methods using modern tools like PyTorch
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Know the differences in compute when using deep learning
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Apply foundational large language models (LLMs) to current use cases
The course covers the entire machine learning lifecycle and the toolkit needed to create robust machine learning pipelines:
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Data preprocessing techniques
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Statistical foundations of ML
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Exploratory Data Analysis
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Python best-practices
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Modern ML libraries
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Model selection and hyperparameter tuning
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Supervised ML algorithms
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Unsupervised techniques
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ML pipelines
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Data bias and how to avoid it in modelling
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Dimensionality reduction toolkit
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Deep Learning with neural networks
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PyTorch application of deep learning
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Bias-Variance Trade-off
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Natural Language Processing (NLP)
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NLP and deep learning applied to generative AI (LLMs)
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Leverage deep learning techniques such as FFNNs, RNNs, LSTMs
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The modern ML lifecycle
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MLOps and ML deployment to production
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. The course provides a balanced combination of theoretical concepts and practical applications.
What will I actually build or produce in the lab?
You’ll complete hands-on notebooks and a small end-to-end project that applies data science and machine learning methods to an engineering-relevant dataset; you’ll leave with reusable code and a brief write-up you can show your team.
How much work is there outside live sessions?
Expect ~1–2 hours of optional practice between sessions; no graded homework is required to earn the certificate unless noted.
Is there an assessment or pass/fail requirement?
This is a non-credit professional program with pass/fail final grade. Successful completion is based on participation and satisfactory completion of in-class assignments.
Is this right if I’m new to machine learning?
It’s designed for professionals who already use Python and basic statistics, but doesn’t require experience with machine learning.
Do I need experience with deep learning or LLMs?
No deep learning or LLM experience; we introduce PyTorch and generative-AI workflows in the context of engineering problems.
What are the daily hours and time zone?
Sessions follow Pacific Time; daily start/end times are listed on this page. Please plan to attend live.
What if I miss a session? Because sessions are live and highly interactive, recordings are not provided. Contact us for options if an emergency occurs.
How large is the cohort?
We prefer smaller, more intimate courses so that students can interact with each other and get feedback from the instructor during live coding and breakouts. Each class can have around 15 to 20 students.
What software or hardware do I need?
A laptop capable of running modern browsers, a webcam/headset, and current Python. We leverage Google Collab for the environment which is free; no local GPU is required.
Which libraries and tools are used?
Python, NumPy/Pandas/Scikit-learn, PyTorch for deep learning, and reproducible workflows that introduce LLM/GenAI usage and MLOps concepts.
How can I bring this course to my company? Email us to schedule a discussion: execed@caltech.edu. We deliver private enterprise or group programs—as-is or customized—on-site or live online. We’ll set up a brief scoping call and send a tailored proposal.
