Amazon free courses in Generative AI and Machine Learning through its “AI Ready” initiative, aiming to equip 2 million individuals globally with essential AI skills by 2025. These courses cater to a wide audience, from beginners to advanced learners, and cover topics like generative AI applications, machine learning fundamentals, and AI project planning.Â
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About the
Amazon doing business as Amazon is an American multinational technology company, engaged in e-commerce, cloud computing, online advertising, digital streaming, and artificial intelligence. It is considered one of the Big Five American technology companies; the other four are Alphabet (parent company of Google), Apple, Meta (parent company of Facebook), and Microsoft.
Amazon was founded on July 5, 1994, by Jeff Bezos in Bellevue, Washington.The company originally started as an online marketplace for books but gradually expanded its offerings to include a wide range of product categories. This diversification led to it being referred to as “The Everything Store”.
The company has multiple subsidiaries, including Amazon Web Services, providing cloud computing, Zoox, a self-driving car division, Kuiper Systems, a satellite Internet provider, and Amazon Lab126, a computer hardware R&D provider. Other subsidiaries include Ring, Twitch, IMDb, and Whole Foods Market. Its acquisition of Whole Foods in August 2017 for US$13.4 billion substantially increased its market share and presence as a physical retailer
Amazon has a reputation as a disruptor of industries through technological innovation and aggressive reinvestment of profits into capital expenditures. As of 2023, it is the world’s largest online retailer and marketplace, smart speaker provider, cloud computing service through AWS, live-streaming service through Twitch, and Internet company as measured by revenue and market share.[14] In 2021, it surpassed Walmart as the world’s largest retailer outside of China, driven in large part by its paid subscription plan, Amazon Prime, which has close to 200 million subscribers worldwide. It is the second-largest private employer in the United States.
Here Are Amazon Free Courses
1. Foundations of Prompt Engineering
In this course, you will learn the principles, techniques, and best practices for designing effective prompts. This course introduces the basics of prompt engineering and progresses to advanced prompt techniques. You will also learn how to guard against prompt misuse and how to mitigate bias when interacting with FMs.
Course level: Intermediate
Duration: 4 hours
Activities
This course includes eLearning interactions.
Course objectives
In this course, you will learn to:
Define prompt engineering and apply general best practices when interacting with FMs
Identify the basic types of prompt techniques, including zero-shot and few-shot learning
Apply advanced prompt techniques when necessary for your use case
Identify which prompt techniques are best suited for specific models
Identify potential prompt misuses
Analyze potential bias in FM responses and design prompts that mitigate that bias
Course Link Click Here
2. Machine Learning on AWS
With Amazon SageMaker Data Wrangler and Amazon SageMaker Autopilot, data and research analysts can prepare data, train, and deploy machine learning (ML) models with minimal coding. You will learn to build ML models for tabular and time series data without deep knowledge of ML. You will also review the best practices for using SageMaker Data Wrangler and SageMaker Autopilot.
After completing this course, you will be able to build ML models to support proofs of concept (POCs). You will also be able to assist data scientists with potential ML model candidates to solve business problems.
Course level: Intermediate
Duration: 4 hours
Activities
This course includes eLearning interactions and knowledge checks.
Course objectives
In this course, you will learn to:
Describe ML concepts and life cycle phases
Describe metrics used for evaluating model candidates
Use SageMaker Data Wrangler to prepare tabular and time series data for training an ML model
Use SageMaker Autopilot to automatically build ML models and identify the best model from a list of model candidates based on your objective metric
Describe best practices for using SageMaker Data Wrangler and SageMaker Autopilot
Course Link Click Here
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3. Building Language Models on AWS
Amazon SageMaker helps data scientists prepare, build, train, deploy, and monitor machine learning (ML) models. SageMaker brings together a broad set of capabilities, including access to distributed training libraries, open source models, and foundation models (FMs). This course introduces experienced data scientists to the challenges of building language models and the different storage, ingestion, and training options to process a large text corpus. The course also discusses the challenges of deploying large models and customizing foundational models for generative artificial intelligence (generative AI) tasks using Amazon SageMaker Jumpstart.
Course level: Advanced
Duration: 5.5 hours
Activities
This course includes text instruction, illustrative graphics, knowledge check questions, and video demonstrations of labs you can run in your own Amazon Web Services (AWS) account.
Course objectives
After completing this course, data scientists can confidently build, train, and tune
performant language models on AWS using SageMaker.
In this course, you will learn to do the following:
Apply best practices for storing and ingesting a large amount of text data to support distributed training
Explore data parallelism and model parallelism libraries to support distributed training on SageMaker
Explain the options available on SageMaker to improve training performance, such as Amazon SageMaker Training Compiler and Elastic Fabric Adapter (EFA)
Explore large language model (LLM) optimization techniques for effective model deployment
Demonstrate how to fine-tune foundational models available on SageMaker Jumpstart
Course Link Click Here
4. Amazon Transcribe Getting Started
Amazon Transcribe is a fully managed artificial intelligence (AI) service that helps you convert speech to text using automatic speech recognition (ASR) technology. In this Getting Started course, you will learn about the benefits, features, typical use cases, technical concepts, and costs of Amazon Transcribe. You will review an architecture for a transcription solution using Amazon Transcribe that you can further adapt to your use case. Through a guided tutorial consisting of narrated video, step-by-step instructions, and transcripts, you will also try real-time and batch transcription in your own Amazon Web Services (AWS) account.
Course level: Fundamental
Duration: 1.5 hours
Activities:
This course includes presentations, graphics, and a step-by-step tutorial to follow along.
Course objectives:
In this course, you will do the following:
Understand how Amazon Transcribe works.
Familiarize yourself with basic concepts of Amazon Transcribe.
Recognize the benefits of Amazon Transcribe.
List typical use cases for Amazon Transcribe.
Describe the typical architectures associated with an Amazon Transcribe solution.
Specify what it would take to implement Amazon Transcribe in a real-world scenario.
Understand the cost structure of Amazon Transcribe.
Implement a demonstration of Amazon Transcribe in the AWS Management Console.
Course Link Click Here
5. Building Generative AI Applications Using Amazon Bedrock
This course is designed for data scientists and machine learning developers who are interested in building generative artificial intelligence (generative AI) applications using either the Amazon Bedrock API or LangChain integration. In this course, you will learn about the architecture patterns to build applications for key generative AI use cases. The modules in this course prepare you to work through examples of generating and summarizing text, question answering, and a chatbot.
Using your own AWS account, you can run practice the use of Amazon Bedrock API calls, SDKs, and open source tools such as LangChain.
Course level: Advanced
Duration: 8 hours (4 hours of lessons and 4 hours of labs)
Activities
This course includes eLearning interactions, knowledge checks, and labs.
Course objectives
In this course, you will learn to:
Identify the components of a generative AI application and how to customize a foundation model (FM)
Describe Amazon Bedrock foundation models, inference parameters, and key Amazon Bedrock APIs
Identify Amazon Web Services (AWS) offerings that help with monitoring, securing, and governing your Amazon Bedrock applications
Describe how to integrate LangChain with large language models (LLMs), prompt templates, chains, chat models, text embeddings models, document loaders, retrievers, and Agents for Amazon Bedrock
Describe architecture patterns that can be implemented with Amazon Bedrock for building generative AI applications
Apply the concepts to build and test sample use cases that leverage the various Amazon Bedrock models, LangChain, and the Retrieval Augmented Generation (RAG) approach
Course Link Click Here
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6. Generative AI Learning Plan for Decision Makers
A Learning Plan pulls together training content for a particular role or solution and organizes those assets from foundational to advanced.  Use Learning Plans as a starting point to discover training that matters to you.
This learning plan is designed to introduce generative AI to business and technical decision-makers. The digital training included in this learning plan will provide an overview of generative AI, and the approach to plan a generative AI project and to build a generative AI-ready organization.
Are you wondering why your completion percentage has changed when you haven’t completed any new training? It changes as you complete training, and when we add, remove, and update training content.
Course Link Click Here
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