AWS Certified AI Practitioner AIF-C01 - Hand…

EARLY ACCESS. This course will be updated as more information about the exam is released by Amazon.

Get certified by Amazon for your knowledge of artificial intelligence and machine learning. It's hard to imagine a certification that would carry more weight in today's era of generative AI.

The AWS Certified AI Practitioner (AIF-C01) exam isn't just for developers - it's aimed at a wide variety of roles in the technology space. Whether you're a PM, manager, sales or marketing professional, or developer - the concepts behind artificial intelligence, GenAI, and machine learning (ML) aren't as hard as you think. This course starts with the basics, explaining things in plain English and with simple examples. No coding required.

We will go deep for those who want it, though. Hands-on activities will give you practice in building a custom chatbot using Amazon Bedrock and Knowledge Bases, building Guardrails for AI safety, and building a fully-fledged LLM agent armed with tools to extend an AI application - all just using the AWS console on the web. And demo videos walk you through training and using machine learning models using Amazon SageMaker.

Unlike other AWS certifications, there is a big business focus on this one. In addition to how the tech works, we'll also review best practices for the entire AI and machine learning lifecycle, the dimensions of "responsible AI," and best practices for security and governance with AI.

Some topics we'll cover include:

Fundamental concepts and terminologies of AI, ML, and Generative AI
How deep learning and large language models (LLM's) work
Evaluating and measuring AI models
Machine learning design principles
Machine learning operations (MLOps)
Use cases of AI, ML, and GenAI
Prompt engineering
Building machine learning pipelines with SageMaker
Common machine learning algorithms
Building generative AI applications with Amazon Bedrock
Retrieval Augmented Generation (RAG)
LLM Agents
Amazon Q Business, Q Developer, and Q Apps
High-level AWS AI and machine learning services
Model training and fine-tuning techniques
Responsible AI
AI Governance and Security

Although this is a course about AI, it is not AI-generated. You'll learn from a real human instructor, with real human experience in building AI applications in a professional setting.

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Instructor

Hey, I'm Frank Kane. I spent nine years working for Amazon from the inside as a senior engineer and senior manager, and I'm best known for my top-selling courses in "big data", data analytics, machine learning, AI, Apache Spark, system design, and Elasticsearch.

I've been teaching on Udemy since 2015, where I've reached over 850,000 students all around the world.

I've worked hard to keep this course up to date with the latest developments in AWS's machine learning and artificial intelligence technologies, and to make sure you're prepared for the latest version of this exam. Let's dive in and get you ready.

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This course also comes with:

Lifetime access to all future updates
9+ hours of video training
80 quiz questions to assess your readiness
A responsive instructor in the Q&A Section
Udemy Certificate of Completion Ready for Download
A 30 Day "No Questions Asked" Money Back Guarantee.

Join us in this course if you want to pass the AWS Certified AI Practitioner exam and master the world of AI and machine learning on the AWS platform.

What's inside

Learning objectives

What to expect on the aws certified ai practitioner aif-c01 exam
Artificial intelligence, generative ai, and machine learning fundamentals
Machine learning design principles
Use cases for ai, generative ai (genai) and machine learning
Building machine learning systems and mlops with sagemaker

Building generative ai systems with amazon bedrock
Amazon q business and q developer
Model training and fine-tuning techniques with aws
Evaluating and measuring your machine learning models
Responsible ai, security, and governance

What to expect on the aws certified ai practitioner aif-c01 exam
Artificial intelligence, generative ai, and machine learning fundamentals
Machine learning design principles
Use cases for ai, generative ai (genai) and machine learning
Building machine learning systems and mlops with sagemaker
Building generative ai systems with amazon bedrock
Amazon q business and q developer
Model training and fine-tuning techniques with aws
Evaluating and measuring your machine learning models
Responsible ai, security, and governance

Syllabus

Introduction

Udemy 101

Get Your Copy of the Slides

Understand the relationship between AI and ML, how machine learning systems are trained, and how deep learning and generative AI works.

Test your knowledge on this section on AI and Machine Learning fundamentals.

Test your knowledge of this section on ML design principles and use-cases.

Test your knowledge in this section on prompt engineering.

Test your knowledge on Amazon SageMaker's features and usage.

Test your knowledge on Bedrock, RAG, Agents, and Amazon Q.

Test your knowledge on AWS's purpose-built AI services.

Review your learnings from this section on model training, tuning, and evaluation.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Provides foundational knowledge of AI and ML, making it suitable for those starting out

Covers various aspects of AI and ML, from fundamentals to practical applications

Includes hands-on activities to provide practical experience in building AI applications

Focuses on business applications of AI and ML, making it relevant for professionals in various roles

Emphasizes responsible AI, governance, and security, which are essential considerations in the industry

Reviews summary

Aws ai practitioner exam prep

According to learners, this course offers a comprehensive and hands-on preparation for the AWS Certified AI Practitioner exam. Many find the instructor's deep expertise and clear, plain English explanations particularly beneficial, especially for those new to AI/ML. The course provides valuable practical experience through its labs on Amazon Bedrock and SageMaker. It covers a wide array of topics from fundamentals to responsible AI, making it suitable for a broad professional audience. Some notes indicate that it was initially in early access, suggesting ongoing updates.

Course content is actively updated to reflect new exam info.

"The instructor mentioned the course is continually updated as new exam information is released."

"It's reassuring to know that the content is kept current with the latest AWS developments."

"I expect the course to evolve and remain relevant for future exam takers."

Includes important best practices for real-world AI implementation.

"I liked the focus on the business aspects and AI lifecycle management."

"The sections on 'responsible AI,' security, and governance were very relevant for professional settings."

"It's good to see best practices for the entire AI and ML lifecycle covered."

Explains complex AI/ML concepts in an accessible manner.

"I appreciated that it starts with the basics, explaining things in plain English."

"The simple examples helped me grasp concepts even without a coding background."

"It really broke down seemingly hard topics, making AI less intimidating."

Thoroughly prepares you for the AWS Certified AI Practitioner exam.

"The course covers all the necessary topics for the AIF-C01 exam, including responsible AI."

"I feel well-prepared for the certification after going through the content and quiz questions."

"It touches upon everything from fundamentals to MLOps, which is crucial for the exam."

Taught by a highly experienced professional with Amazon background.

"I found the instructor's background as a senior engineer at Amazon really adds to his credibility."

"The instructor clearly knows his stuff and delivers complex topics in an understandable way."

"It's great to learn from someone with direct experience building AI applications professionally."

Provides practical experience building AI applications on AWS.

"I really valued the hands-on activities, especially building the chatbot with Amazon Bedrock."

"The demo videos for SageMaker were very helpful in understanding model training."

"Building Guardrails for AI safety was a practical exercise that solidified my understanding."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in AWS Certified AI Practitioner AIF-C01 - Hands On, In Depth! with these activities:

Follow tutorial on Neural Networks

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This tutorial can provide you with a solid foundation in Neural Networks

Browse courses on Neural Networks

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Search and find an accessible tutorial on Neural Networks
Work through the tutorial

Practice Python coding exercises

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This practice will improve your programming skills

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Solve daily coding exercises on Leetcode or other websites
Participate in coding challenges

Join a study group for this course

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This activity will help you to understand the concepts better

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Find a study group or create one with your classmates
Meet regularly and discuss the course material

Two other activities

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Attend a Machine Learning workshop

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This can provide you with the opportunity to learn from experts

Browse courses on Machine Learning

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Search for a Machine Learning workshop in your area
Register and attend the workshop

Create a presentation on Machine Learning topics

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This activity will help you to identify important concepts and consolidate your knowledge

Browse courses on Machine Learning

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Choose a topic from Machine Learning
Research and gather information on the topic
Create a PowerPoint presentation using visuals, examples, and clear explanations

Career center

Learners who complete AWS Certified AI Practitioner AIF-C01 - Hands On, In Depth! will develop knowledge and skills that may be useful to these careers: