AWS Free Courses with Certificate 2024: Free AI and Machine Learning Courses Apply Now

The cloud computing landscape is a continuous innovation engine, with Amazon Web Services (AWS) leading the charge. For aspiring individuals eager to enter the fascinating world of Artificial Intelligence (AI) and Machine Learning (ML), AWS free courses with certificates in 2024. This comprehensive guide delves into the best free AWS courses you can leverage to gain valuable knowledge and industry-recognized credentials, propelling your career forward in the ever-evolving tech landscape

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About the AWS

Amazon Web Services, Inc. (AWS) is a subsidiary of Amazon that provides on-demand cloud computing platforms and APIs to individuals, companies, and governments, on a metered, pay-as-you-go basis. Clients will often use this in combination with autoscaling (a process that allows a client to use more computing in times of high application usage, and then scale down to reduce costs when there is less traffic).

These cloud computing web services provide various services related to networking, computing, storage, middleware, IoT and other processing capacity, as well as software tools via AWS server farms. This frees clients from managing, scaling, and patching hardware and operating systems. One of the foundational services is Amazon Elastic Compute Cloud (EC2), which allows users to have at their disposal a virtual cluster of computers with extremely high availability, which can be interacted with over the internet via rest APIs, a CLI, or the AWS console.

AWS’ virtual computers emulate most of the attributes of a real computer, including hardware central processing units (CPUs) and graphics processing units (GPUs) for processing; local/RAM; Hard-disk (HDD)/SSD storage; a choice of operating systems, networking; and pre-loaded application software such as web servers, databases, and customer relationship management (CRM).

Two new AI certifications will be released by AWS in August.

One certification, AWS Certified AI Practitioner, covers the fundamentals of artificial intelligence and machine learning, design considerations, model tuning and more. Since this is an AWS course, you should have some experience with AWS before you start; however, you don’t need to have built AI/ML solutions on AWS before. This certification costs $75 and concludes with a 120-minute exam. Enrollment opens on August 21.

The second certification, AWS Certified Machine Learning Engineer – Associate, is designed for people who already have about a year of experience designing AI/ML on AWS. This certification teaches technical skills regarding ML workloads. It covers feature engineering, model training, model integration and deployment, security and more. AWS Certified Machine Learning Engineer – Associate will be released in beta, which means lessons from the first round of participants may be used to create a standard version of the exam later. The beta version is open for enrollment on August 13. It will cost $75 and include a 170-minute exam.

The certifications can then be added to a resume to show your AI knowledge has been verified by AWS.

Eligibility Criteria

Aws Free Courses with Certificate for Everyone Like Job Professionals, Housewives, etc.

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Here are Aws Free Courses with Certificate 2024

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. Low-Code Machine Learning on AWS

Note: This course will expire on July 30, 2024. If you would like to complete this course, please do so before that date. This course has been replaced by No-code Machine Learning and Generative AI on AWS  (available on June 26, 2024).

If you are a subscriber to AWS Skill Builder, or would like to subscribe, there is another course version with labs available as of June 26, 2024.  The course is No-code Machine Learning and Generative AI on AWS (With Labs)

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

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

Course Description:

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 embedding 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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