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Courses for learners building core AI fluency.
Agent systems, workflow orchestration, and practical tooling.
Quick-hit courses for a focused weekend sprint.
This course module teaches best practices for using automated machine learning (AutoML) tools in your machine learning workflow, including benefits and limitations and common AutoML patterns that can be used in projects.
Experienced builders who need a deeper technical track.
30 minutes
Self paced
Advanced
Certificate available
English
recent
Official-source checked
This course module teaches the key concepts of embeddings, and techniques for training an embedding to translate high-dimensional data into a lower-dimensional embedding vector.
Experienced builders who need a deeper technical track.
45 minutes
Self paced
This course module teaches key principles of ML Fairness, including types of human bias that can manifest in ML models, identifying and mitigating these biases, and evaluating for these biases using metrics including demographic parity, equality of opportunity, and counterfactual fairness.
Experienced builders who need a deeper technical track.
110 minutes
Self paced
This course module provides an overview of language models and large language models (LLMs), covering concepts including tokens, n-grams, Transformers, self-attention, distillation, fine-tuning, and prompt engineering.
Experienced builders who need a deeper technical track.
45 minutes
Self paced
This course module teaches key considerations and best practices for putting an ML model into production, including static vs. dynamic training, static vs. dynamic inference, transforming data, and deployment testing and monitoring.
Experienced builders who need a deeper technical track.
70 minutes
Self paced
This course module teaches the fundamental concepts and best practices of working with categorical data, including encoding methods such as one-hot encoding and hashing, creating feature crosses, and common pitfalls to look out for.
Experienced builders who need a deeper technical track.
50 minutes
Self paced
This course module teaches fundamental concepts and best practices for working with numerical data, from how data is ingested into a model using feature vectors to feature engineering techniques such as normalization, binning, scrubbing, and creating synthetic features with polynomial transforms.
Experienced builders who need a deeper technical track.
85 minutes
Self paced
Advanced
Certificate available
English
recent
Official-source checked
Advanced
Certificate available
English
recent
Official-source checked
Advanced
Certificate available
English
recent
Official-source checked
Advanced
Certificate available
English
recent
Official-source checked
Advanced
Certificate available
English
fresh
Official-source checked
Advanced
Certificate available
English
fresh
Official-source checked