[NEW] AWS Certified AI Practitioner AIF-C01 from Udemy

Discover the future of Artificial Intelligence (AI) and Machine Learning (ML) with AWS. In this course, designed for the AWS Certified AI Practitioner exam, you will learn the fundamentals of artificial intelligence, machine learning, and deep learning, applied through AWS’s advanced services. This course is focused on equipping you with the tools needed to understand, implement, and leverage AI solutions in the real world.

Throughout the modules, you will explore essential concepts, practical use cases, and best practices for working with advanced technologies like generative AI. Additionally, we will delve into the importance of applying AI responsibly and securely, following industry standards.

This is NOT a boring course of voice and PowerPoint lectures. Here I will discuss and present the material in an interactive and engaging style that will keep you interested and make it easier to understand. Check out the free videos available and you will see the difference.

What will you learn?

Fundamentals of Artificial Intelligence and Machine Learning (ML)You will understand the basics of AI and ML, including neural networks, computer vision, and natural language processing (NLP). We will examine the key differences between artificial intelligence, machine learning, and deep learning, and learn how to identify when it’s appropriate to apply these technologies.
Generative Artificial IntelligenceYou will discover how generative AI can create new content, such as text, images, and audio, from existing data. We’ll see examples of generative models and their practical applications across industries, such as creative content generation, software development, and much more.
Foundation Models and Fine-TuningYou will learn about pre-trained models and how to choose the right one for different scenarios. Additionally, you will explore fine-tuning techniques to optimize model performance and how to customize them for specific use cases.
Responsible Artificial IntelligenceYou will understand the ethical principles of AI, including transparency, privacy, and bias mitigation. This module will also cover the tools AWS offers to ensure models are secure, explainable, and adhere to responsibility standards.
Security, Compliance, and Governance for AI SolutionsYou will learn how to implement governance and security strategies for AI solutions, ensuring systems meet regulatory and best practice standards. This includes handling data securely and protecting models from potential vulnerabilities.

Course Contents and Domain Distribution

The course is aligned with the five key domains of the AWS Certified AI Practitioner exam, providing a solid foundation to help you achieve certification. These domains are distributed as follows:

Domain 1: Fundamentals of AI and ML (20% of scored content)
Domain 2: Fundamentals of Generative AI (24% of scored content)
Domain 3: Applications of Foundation Models (28% of scored content)
Domain 4: Guidelines for Responsible AI (14% of scored content)
Domain 5: Security, Compliance, and Governance for AI Solutions (14% of scored content)

Who is this course for?

This course is designed for anyone looking to gain a solid understanding of the principles of artificial intelligence and machine learning with AWS. You don’t need to be an expert in programming or advanced mathematics; the course covers everything from basic concepts to more advanced applications, all in an accessible way. It is ideal for:

Professionals seeking to enter the field of artificial intelligence
Developers looking to implement AI solutions on AWS
Business leaders who want to integrate AI into their projects
Candidates for the AWS Certified AI Practitioner exam

Prerequisites

No prior technical knowledge in AI or machine learning is required, but basic familiarity with AWS services and cloud computing concepts will help you get the most out of the course content.

What's inside

Learning objectives

Pass the aws certified ai practitioner exam (aif-c01)
Master key concepts of ai, machine learning, and generative ai.
Identify real-world ai use cases in amazon web services.

Understand responsible practices and security in ai solutions.
And more...

Pass the aws certified ai practitioner exam (aif-c01)
Master key concepts of ai, machine learning, and generative ai.
Identify real-world ai use cases in amazon web services.
Understand responsible practices and security in ai solutions.
And more...

Syllabus

Introduction

INTRO

About the AWS Certified AI Practitioner exam

Creating an AWS Account

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Covers the five key domains of the AWS Certified AI Practitioner exam, providing a solid foundation to help learners achieve certification and demonstrate their expertise

Explores generative AI, including how it can create new content like text, images, and audio, which is a rapidly growing area with significant career opportunities

Examines the ethical principles of AI, including transparency, privacy, and bias mitigation, which are increasingly important considerations in the field

Teaches how to implement governance and security strategies for AI solutions, ensuring systems meet regulatory and best practice standards, which is crucial for real-world applications

Requires basic familiarity with AWS services and cloud computing concepts, which may necessitate additional learning for individuals without prior experience in these areas

Includes hands-on labs with services like Amazon Q Business, Bedrock, Rekognition, SageMaker, and A2I, which may require an AWS account and incur costs for usage beyond free tiers

Reviews summary

Aws ai practitioner exam prep course

According to learners, this course provides a strong foundation for the AWS Certified AI Practitioner exam. Learners appreciate the clear explanations and say it's perfect for beginners with no prior AI/ML knowledge. The instructor's style is engaging. While it covers key concepts like Generative AI and Foundation Models and their AWS applications, some wish for more in-depth practical labs or deeper dives into specific AWS services. It is generally considered effective preparation for the AIF-C01 exam.

Instructor style is well-received.

"The instructor was engaging and made the learning process enjoyable."

"I appreciated the instructor's presentation style, it kept me interested."

"The instructor is knowledgeable and presents the material effectively."

Concepts explained simply, suitable for beginners.

"The instructor explains complex topics very clearly and provides easy-to-follow examples."

"I found the explanations for difficult concepts in AI/ML to be very clear."

"The way the material was presented made understanding the fundamentals much easier."

Effectively covers AIF-C01 syllabus.

"This course gave me a great overview and covered the key topics clearly for the exam."

"As a beginner, I found this course structured perfectly and it really prepared me for the AIF-C01."

"I feel well-prepared for the AWS Certified AI Practitioner exam after completing this course."

Could use more practical labs/service demos.

"Some parts felt a bit theoretical; I wish there were more hands-on sections."

"While the theory is solid, I needed more practical examples using the actual AWS services."

"The course could be improved with more in-depth labs demonstrating the services."

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 [NEW] AWS Certified AI Practitioner AIF-C01 with these activities:

Review Basic Machine Learning Concepts

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Solidify your understanding of fundamental machine learning concepts before diving into AWS-specific implementations.

Browse courses on Machine Learning Fundamentals

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Review online resources covering ML basics.
Complete a basic ML tutorial.
Take a practice quiz on ML concepts.

Read 'Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow'

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Gain a deeper understanding of the machine learning algorithms that power AWS AI services.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

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Read the chapters relevant to the course syllabus.
Work through the code examples in the book.
Relate the concepts to AWS services.

Practice Prompt Engineering Techniques

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Improve your prompt engineering skills by practicing different techniques with foundation models on Amazon Bedrock.

Show steps

Access Amazon Bedrock and select a foundation model.
Experiment with different prompt engineering techniques.
Evaluate the output and refine your prompts.
Document your findings and best practices.

Four other activities

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Show all seven activities

Read 'Generative AI with AWS'

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Deepen your knowledge of generative AI and its applications on AWS.

View Generative AI on AWS: Building Context-Aware... on Amazon

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Read the chapters on generative AI models and techniques.
Explore the code examples and implement them on AWS.
Experiment with different generative AI services on AWS.

Build a Simple Image Classifier with Amazon Rekognition

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Apply your knowledge of Amazon Rekognition to build a practical image classification project.

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Set up an AWS account and configure Rekognition.
Gather a dataset of images for classification.
Use Rekognition to label and classify the images.
Evaluate the performance of the classifier.

Create a Blog Post on Responsible AI on AWS

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Reinforce your understanding of responsible AI principles by writing a blog post explaining how to implement them on AWS.

Show steps

Research AWS services for responsible AI.
Outline the key principles of responsible AI.
Write a blog post explaining how to apply these principles on AWS.
Publish the blog post on a platform like Medium.

Create a Presentation on AI Security Best Practices

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Solidify your understanding of AI security by creating a presentation on best practices for securing AI solutions on AWS.

Show steps

Research security best practices for AI systems.
Identify AWS services and features for securing AI.
Create a presentation outlining these best practices.
Present the presentation to peers or colleagues.

Career center

Learners who complete [NEW] AWS Certified AI Practitioner AIF-C01 will develop knowledge and skills that may be useful to these careers:

Artificial Intelligence Engineer

An Artificial Intelligence Engineer designs, develops, and deploys AI models and systems. This course is directly relevant because it covers the fundamentals of AI, machine learning, and deep learning, emphasizing their application through AWS services. The curriculum addresses key areas such as neural networks, computer vision, natural language processing, and generative AI, providing an understanding of the technologies used every day by an AI Engineer. You'll learn how to implement responsible AI practices and apply security, compliance, and governance strategies for AI, all skills required for building robust and trustworthy AI solutions.

See salaries and explore the career path for Artificial Intelligence Engineer

Machine Learning Engineer

A Machine Learning Engineer builds and maintains machine learning models and infrastructure. This course helps build a foundation for this role by teaching the core concepts of machine learning, model training, tuning, and deployment, all within the AWS ecosystem. Machine Learning Engineers must understand the machine learning development lifecycle, which includes data collection, preprocessing, model evaluation, and monitoring—all topics covered in the course. Understanding how to optimize models with techniques such as retrieval augmented generation and fine-tuning is crucial for a Machine Learning Engineer, and this course teaches just that.

See salaries and explore the career path for Machine Learning Engineer

Generative AI Specialist

A Generative AI Specialist focuses on creating new content using generative models. This course goes in-depth on the fundamentals of generative AI, covering concepts like foundation models, diffusion models, and generative adversarial networks. It explores the practical applications of these models as well as their limitations. This knowledge is essential for a Generative AI Specialist, who will be working on cutting-edge AI projects. This course also covers the entire foundation model lifecycle and helps in choosing the most appropriate model to work with.

See salaries and explore the career path for Generative AI Specialist

AI Solutions Architect

An AI Solutions Architect designs and plans how to implement AI systems within an organization. This course provides an understanding of the full breadth of AI and ML concepts within the AWS environment, as well as responsible AI guidelines and security measures, which are crucial for an AI Solutions Architect. The curriculum, including how to choose pre-trained models, optimize them, and apply governance strategies, enables an AI Solutions Architect to make informed decisions and design scalable and responsible AI solutions. A course that emphasizes the importance of responsible AI helps architects create ethical systems.

See salaries and explore the career path for AI Solutions Architect

AI Consultant

An AI Consultant advises organizations on how to integrate AI solutions. This course covers the fundamentals of AI and ML in the context of AWS's offerings. An AI Consultant needs a strong understanding of different AI technologies and how they can be applied in various industries, a skill that is developed in this course. The course also explains responsible AI, security, compliance, and governance for AI solutions. This will help an AI Consultant recommend ethical, secure, and compliant AI systems to their clients.

See salaries and explore the career path for AI Consultant

AI Product Manager

An AI Product Manager guides the development and launch of AI products. This course helps an AI Product Manager understand the underlying technologies of AI and machine learning, such as generative AI and foundation models. It also discusses business use cases for AI. An AI Product Manager will be able to make more informed decisions about the feasibility and potential of AI solutions, and can better communicate with engineers, data scientists, and business stakeholders. This course also covers the management aspects of AI, which helps with strategy and implementation.

See salaries and explore the career path for AI Product Manager

AI Project Manager

An AI Project Manager oversees the planning and execution of AI projects. This course provides an understanding of the entire AI project lifecycle, from selecting the right machine learning techniques to implementing responsible AI practices and governance. This enables an AI Project Manager to manage AI projects effectively. The course also covers the ethical dimensions of deploying AI, which allows an AI Project Manager to ensure the projects are responsible and compliant. Understanding the technical aspects of AI increases the clarity of project execution.

See salaries and explore the career path for AI Project Manager

Data Scientist

A Data Scientist analyzes data to extract insights and build predictive models. While not solely focused on data analysis, this course on AI fundamentals helps a Data Scientist understand how to leverage machine learning and AI techniques to improve their work. A Data Scientist will benefit from the exploration of AI terms, machine learning lifecycle, and model selection. This course also provides knowledge of responsible AI, ensuring that the models built do not perpetuate biases, something a Data Scientist must keep in mind in his or her work.

See salaries and explore the career path for Data Scientist

Cloud Solutions Engineer

A Cloud Solutions Engineer develops and manages cloud-based solutions. This course introduces AI and ML techniques as implemented with AWS. It may be useful to a Cloud Solutions Engineer who wishes to work with more complex projects. The course covers aspects of machine learning lifecycle, and AWS AI services, which are key to understanding how these technologies are integrated within cloud environments. With this knowledge, a Cloud Solutions Engineer can facilitate the deployment and maintenance of AI solutions within cloud infrastructure.

See salaries and explore the career path for Cloud Solutions Engineer

Software Developer

A Software Developer writes code for applications. Though not exclusively focused on development, this course provides a solid foundation for a software developer who wishes to incorporate AI functionalities into their projects using AWS. The course introduces AI concepts, machine learning lifecycle, generative AI, and how to build responsible AI systems, allowing the Software Developer to build more effective products. The hands-on portions of the course will be a great introduction to AWS's AI services and help them become more versatile.

See salaries and explore the career path for Software Developer

Technology Analyst

A Technology Analyst evaluates technology trends and their impact. This course helps Technology Analysts understand the concepts, applications, and potential of AI, specifically within the AWS ecosystem. The course's curriculum, covering generative models, responsible AI, and security, allows the Technology Analyst to assess the practical implementation of AI in a business context. A Technology Analyst can also better understand the limitations of AI solutions and potential risks.

See salaries and explore the career path for Technology Analyst

Research Scientist

A Research Scientist engages in advanced research, typically with an advanced degree, in areas such as machine learning and artificial intelligence. Although this course focuses on practical implementation in AWS, the knowledge it provides of AI, machine learning, and generative AI helps build a foundation for this role. Understanding the foundations of machine learning, neural networks, and techniques like fine-tuning and prompt engineering, covered in this course, can inform advanced research. A Research Scientist can benefit from the practical use cases and real-world examples of AI solutions provided in this course.

See salaries and explore the career path for Research Scientist

Business Intelligence Analyst

A Business Intelligence Analyst uses data to improve business decision making. This course may be useful to a Business Intelligence Analyst who wants to understand AI and ML to enhance their role. This course covers key terminology and provides exposure to the capabilities of AI and ML, including generative AI. A Business Intelligence Analyst may use this knowledge to identify opportunities for AI integration within their organization, and improve their ability to analyze results from AI projects.

See salaries and explore the career path for Business Intelligence Analyst

Data Engineer

A Data Engineer is responsible for building and maintaining data pipelines and infrastructure for data processing. This course may be helpful because it introduces the machine learning lifecycle, and the concepts of data management needed for AI solutions. It also allows the Data Engineer to understand the different security and compliance needs of AI systems, which can impact the data infrastructure. This course will allow the Data Engineer to better plan infrastructure for AI and ML projects.

See salaries and explore the career path for Data Engineer

Technical Writer

A Technical Writer creates documentation for technical products and services. Technical writers who want to specialize in AI will be able to learn key concepts using this course. Although not a technical role, a Technical Writer can benefit from learning about the different aspects of AI, machine learning, generative AI, and how to use AI tools within AWS. This course will provide a base upon which an AI specialized Technical Writer can begin their career.

See salaries and explore the career path for Technical Writer

[NEW] AWS Certified AI Practitioner AIF-C01

What's inside

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Syllabus

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Reviews summary

Aws ai practitioner exam prep course

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