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Learn a skill. Earn a certificate. Add it to your resume. Data science, generative AI, project management, cybersecurity, business, design, and more — there's a certificate for every career goal.
Learn to design production AI business applications using data modeling, systems integration, core AI techniques, and security and privacy practices.
Secure AI systems end to end, from training-data poisoning and model theft to prompt injection, supply-chain risks, and incident response.
Learn to handle imbalanced machine-learning datasets through data preparation, baseline modeling, resampling, evaluation metrics, and specialized algorithms for robust, fair models.
Implement a complete reinforcement learning solution end to end, covering RL fundamentals, sample-based learning methods, and prediction and control with function approximation.
Lead AI projects from definition to delivery: frame problems with SMART objectives and metrics, check data readiness, write scope statements and a WBS, and run agile delivery.
This learning path provides a comprehensive introduction to machine learning operations (MLOps), with a specific focus on generative AI.
Apply AI across the full project lifecycle: initiate, plan, execute, monitor and close projects while automating tasks, anticipating risks, and weighing ethical questions.
Use OpenAI Whisper API with Go to transcribe local, remote, and large video files, then build a video summarization system.
Build Python skills by coding through practical, start-to-finish projects in data analysis, predictive modeling, and AI-powered application development.
Learn to deploy AI, big data, and machine learning across marketing, finance, and HR, and design governance frameworks that address AI ethics, risk, and fairness.
Build a multilayer perceptron from scratch in R, implementing neurons, activations, initialization, backpropagation, and a reusable neural network library.
Build recurrent neural networks in PyTorch to forecast univariate and multivariate time series with LSTMs and GRUs.
Implement ensemble, unsupervised, clustering, and neural-network algorithms from scratch in C++ for hands-on AI development.
Build practical AI and data literacy for federal work through prompt frameworks, critical data interpretation, validation checklists, and accountable human oversight.
Build practical AI skills through core concepts, data literacy, and prompt engineering for everyday work and real business challenges.
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