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The popularity of AI is soaring, with businesses across industries harnessing its innovation potential. AWS is pivotal in this trend, offering robust AI solutions and services. AWS courses on AI topics provide invaluable knowledge and skills, empowering individuals to leverage AI effectively enabling them to stay ahead in today’s competitive landscape. This article lists the top AI courses by AWS that equip learners with the skills needed to harness AI effectively, from foundational knowledge to advanced techniques, enabling them to stay ahead in today’s competitive landscape.
Machine Learning Terminology and Process
This course introduces basic machine learning concepts and details each step of the ML process. It covers common terms and techniques used in ML projects, aiming to help you understand and discuss the entire ML process comprehensively.
Practical Data Science with Amazon SageMaker
This =course provides an immersive experience in the life of a data scientist, focusing on ML solutions with Amazon SageMaker on AWS. Learners gain insights into ML processes, roles, and responsibilities, along with hands-on experience in building, training, deploying ML models, and addressing operational challenges.
Machine Learning Essentials for Business and Technical Decision Makers
This foundational course guides learners in understanding the basics of machine learning and evaluating its benefits and risks in business contexts. It covers identifying requirements for successful ML projects and adapting organizations to sustain success with ML adoption.
Low-Code Machine Learning on AWS
This course teaches data and research analysts to use Amazon SageMaker Data Wrangler and Autopilot to prepare data, train, and deploy ML models with minimal coding. It covers building ML models for tabular and time series data, best practices, and supporting proofs of concept to assist data scientists in solving business problems.
Planning a Generative AI Project
This course provides an introduction to the technical foundations and key terminology of generative AI. It guides the planning of a generative AI project and evaluates the risks and benefits involved.
Introduction to Generative AI – Art of the Possible
This is a short, beginner-level course that introduces generative AI, its use cases, risks, and benefits, and it is illustrated with a content generation example. By the end, learners will understand generative AI basics, its advantages and risks, and how to apply content generation in their business.
Amazon CodeWhisperer – Getting Started
This fundamental course teaches how to install and use Amazon CodeWhisperer, a generative AI coding companion that enhances efficiency with code suggestions and security scans. It covers key features such as code completion, open-source tracking, security, and subscription options.
Foundations of Prompt Engineering
This course on prompt engineering covers principles, techniques, and best practices for designing effective prompts, including zero-shot and few-shot learning. It also addresses advanced techniques, identifying suitable prompts for specific models, preventing misuse, and mitigating bias in foundational model responses.
Building Generative AI Applications Using Amazon Bedrock
This is an advanced course designed is for data scientists and ML developers aiming to build generative AI applications using Amazon Bedrock API and LangChain. It covers architecture patterns, integration techniques, and practical labs for generating text, question-answering, and chatbots, with a focus on leveraging AWS tools for monitoring, security, and governance.
Building Language Models on AWS
This course on Amazon SageMaker targets experienced data scientists, focusing on building and optimizing language models. It covers storage, ingestion, and training options for large text corpora, along with deployment challenges and customization of foundational models for generative AI tasks using SageMaker Jumpstart.
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