AWS Launched Free AI and Machine Learning Courses with Certificate

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The future is powered by AI and Machine Learning. Now, Amazon Web Services (AWS) is making it easier than ever to jump on board with the launch of their free AI and Machine Learning Courses! Whether you’re a complete beginner or looking to refine your existing knowledge, this comprehensive program offers a path to develop in-demand skills and propel your career forward.

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AWS Launched Free AI and Machine Learning Courses with Certificate

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AWS to Release two new AI Certifications in August

The principles of machine learning and artificial intelligence, design concerns, model tuning, and more are covered in one certification, the AWS Certified AI Practitioner. It’s not necessary to have built AI/ML solutions on AWS before, but as this is an AWS course, you should have some familiarity with the platform before beginning. There is a $75 certification fee and a 120-minute exam to complete. Admissions begin on August 21.

Those with approximately a year of experience creating AI/ML on AWS are eligible for the second certification, AWS Certified Machine Learning Engineer – Associate. The technical skills related to ML workloads are taught by this certification. Feature engineering, model training, deployment and integration, security, and other topics are covered. Because AWS Certified Machine Learning Engineer – Associate is releasing in beta, it’s possible that later on, a standard version of the test will be developed using input from the initial wave of participants. Enrollment in the beta version begins on August 13. A 170 minute exam is included in the $75 price.

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

Free AI and Machine Learning Courses

To make using its platform simpler, AWS provides a range of free or inexpensive training courses. For developers and technical audiences, Amazon offers the following generative AI training courses through AWS Skill Builder at no cost:

1. Course Foundations of Prompt Engineering

In this course, you will learn the principles, techniques, and best practices for designing effective prompts. AI and Machine Learning 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 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 – Foundations of Prompt Engineering

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2. Course Low-Code 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.

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 – Low-Code Machine Learning on AWS

3. Course 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 AI and Machine Learning (generative AI) tasks using Amazon SageMaker Jumpstart.

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 – Building Language Models on AWS

4. Course Amazon Transcribe Getting Started

Amazon Transcribe is a fully managed AI and Machine Learning (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

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 – Amazon Transcribe Getting Started

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5. Course 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 AI and Machine Learning, 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.

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 and Machine Learning 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 AI and Machine Learning 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 – Building Generative AI Applications Using Amazon Bedrock

6. Course 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 and Machine Learning AI-ready organization.

Course Link – Generative AI Learning Plan for Decision Makers

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