These machine learning and deployment courses can help you build, deploy, and scale models with confidence. Learn key algorithms, cloud tools, automation workflows, and best practices to create efficient, production-ready ML solutions that drive real-world impact.
Want to deploy a machine learning model like a pro? Learn to develop, monitor, and launch a classification model with FastAPI using real-world data. This intermediate-level project gives you practical experience to level up your skills.
Learn to build powerful models in this Machine Learning Nanodegree with PyTorch. Master supervised, unsupervised, and deep learning techniques through projects involving customer segmentation and image classification.
Scale your ML solutions in the cloud. Use Azure Machine Learning Studio, SDK, AutoML, and Azure Pipelines to deploy and monitor models, mastering pipeline automation, Kubernetes security, and API management.
Step into the world of ML engineering with interactive AWS projects. Train models, deploy endpoints, and automate workflows using SageMaker, Lambda, and Step Functions to deliver efficient machine learning applications.
From foundational concepts to advanced algorithms, this Nanodegree equips you with the tools to build intelligent agents using Python, neural networks, and state-of-the-art RL frameworks across robotics, finance, and beyond.
Gain end-to-end expertise in Machine Learning DevOps with this Nanodegree program that focuses on deployment, automation, and monitoring. Develop production-ready skills in CI/CD, container orchestration, and model performance tracking.
Learn to apply unsupervised learning methods like K-means and Gaussian mixtures to extract value from raw data. Develop skills in feature extraction and cluster validation to enhance data analysis.
Refine your machine learning skills with this AWS Machine Learning Engineer Nanodegree. Learn to deploy models on SageMaker and design automated workflows with AWS Lambda and Step Functions.
From theory to application, this course guides you through supervised learning essentials. Learn to select, implement, and refine models that solve complex classification and regression tasks.
Build powerful machine learning models to predict outcomes and uncover insights using data. This Nanodegree covers foundational supervised learning techniques like linear regression, decision trees, Naive Bayes, SVMs, and perceptrons.
Combining Machine Learning fundamentals with cloud platforms, DevOps, and continuous deployment courses equips you with the tools to build, deploy, and manage intelligent applications end-to-end. This integrated approach boosts efficiency, automates workflows, and drives faster innovation.
Enhance your DevOps skills with these complimentary courses. Master application deployment and CI/CD to automate releases, adopt agile methodologies to improve collaboration and delivery, and deepen your cloud computing and architecture knowledge to build scalable, efficient infrastructure.
Strengthen your cloud expertise by learning agile methodologies, advanced deployment, and multi-cloud strategies. These complementary courses will equip you with practical skills to boost your ability to design, manage, and optimize cloud solutions seamlessly across AWS and Azure.
By expanding your skills across different areas of product development, animation, and generative AI, you’ll gain complementary expertise that can take your deployment projects to the next level, adding both technical depth and creative value.
Explore all of Udacity’s Schools, consisting of hundreds of career-driven programs and courses that are designed to teach practical skills and help you learn to your full potential.