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[NEW] AWS Certified AI Practitioner AIF-C01

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  • 1 -INTRO.mp4
    02:20
  • 2 -About the AWS Certified AI Practitioner exam.mp4
    05:01
  • 3 -Creating an AWS Account.mp4
    05:29
  • 4 -AWS Budgets.mp4
    05:46
  • 5 -AWS Cost Explorer.mp4
    03:55
  • 6 -The birth of Artificial Intelligence.mp4
    09:39
  • 1 -AWS-Certified-AI-Practitioner Exam-Guide.pdf
  • 1 -Amazon Q Business Getting Started Files.zip
  • 1 -PRESENTACION AI PRACTITIONER.pdf
  • 1 - Download study material.html
  • 1 -Intro Domain 1 - Fundamentals of Machine Learning and Artificial Intelligence.mp4
    02:13
  • 2 -Basic AI terms (AI, ML, deep learning, neural networks, computer vision, etc) P1.mp4
    08:40
  • 3 -Basic AI terms (AI, ML, deep learning, neural networks, computer vision, etc) P2.mp4
    07:39
  • 4 -Similarities and differences between AI, ML, and deep learning.mp4
    05:27
  • 5 -Inferences, Data and Learning Techniques in AI. PARTE 1.mp4
    06:14
  • 6 -Inferences, Data and Learning Techniques in AI. PARTE 2.mp4
    04:38
  • 7 -Inferences, Data and Learning Techniques in AI. PARTE 3.mp4
    03:04
  • 8 -Recognizing the applications where AIML can add value.mp4
    03:05
  • 9 -Determining when AIML solutions are not appropriate.mp4
    02:22
  • 10 -Selecting the appropriate ML techniques for specific use cases.mp4
    05:39
  • 11 -Practical AI use cases - AWS managed AIML services. PART 1.mp4
    05:13
  • 12 -Practical AI use cases - AWS managed AIML services. PART 2.mp4
    04:52
  • 13 -Examples of real-world AI applications.mp4
    04:55
  • 14 -Machine Learning Development Lifecycle. PART 1 (ML pipeline, lifecycle, etc).mp4
    06:18
  • 15 -Machine Learning Development Lifecycle. PART 2 (data collection, data pre, etc).mp4
    06:00
  • 16 -Machine Learning Development Lifecycle. PART 3 (model training, tuning).mp4
    03:31
  • 17 -Machine Learning Development Lifecycle. PART 4 (Evaluation).mp4
    07:01
  • 18 -Machine Learning Development Lifecycle. PART 5 (Evaluation).mp4
    06:17
  • 19 -Machine Learning Development Lifecycle. PART 6 (Deployment).mp4
    04:24
  • 20 -Machine Learning Development Lifecycle. PART 7 (Monitoring).mp4
    03:13
  • 21 -Fundamental concepts of ML operations (MLOps).mp4
    04:05
  • 1 -INTRO.mp4
    01:39
  • 2 -Foundational Generative AI concepts.mp4
    11:00
  • 3 -Foundation Models (FM).mp4
    03:51
  • 4 -Multi-modal Models.mp4
    02:29
  • 5 -Generative Adversarial Networks (GANs).mp4
    06:34
  • 6 -Variations Generative Adversarial Networks (GANs).mp4
    03:28
  • 7 -Diffusion Models.mp4
    02:25
  • 8 -Potential use cases for Generative AI models.mp4
    06:35
  • 9 -Foundation model lifecycle (Generative AI).mp4
    05:30
  • 10 -Advantages of Generative AI.mp4
    02:58
  • 11 -Disadvantages of Generative AI solutions.mp4
    03:06
  • 12 -Factors to select appropriate Generative AI models.mp4
    05:13
  • 13 -Business value and metrics for Generative AI applications.mp4
    04:57
  • 14 -AWS services and features to develop Generative AI applications.mp4
    03:27
  • 15 -Advantages and Benefits of AWS AI solutions.mp4
    03:11
  • 16 -Cost tradeoffs of AWS Generative AI services.mp4
    04:38
  • 17 -AWS AIMLGen AI services stack.mp4
    03:24
  • 1 -INTRO.mp4
    01:35
  • 2 -Criteria to choose pre-trained models.mp4
    05:45
  • 3 -Retrieval Augmented Generation (RAG) and its business applications.mp4
    06:50
  • 4 -Optimizing Foundation Models with RAG.mp4
    08:36
  • 5 -Optimizing Foundation Models with fine-tuning.mp4
    11:55
  • 6 -INTRO - Prompt Engineering.mp4
    02:27
  • 7 -Concepts and constructs of prompt engineering.mp4
    03:22
  • 8 -Modifying prompts.mp4
    04:51
  • 9 -Best practices for prompt engineering.mp4
    05:06
  • 10 -Prompt engineering techniques.mp4
    08:03
  • 11 -Potential risks and limitations of prompt engineering.mp4
    06:20
  • 1 -Intro.mp4
    01:17
  • 2 -Responsible AI.mp4
    04:26
  • 3 -Responsible AI Challenges.mp4
    05:20
  • 4 -Core dimensions of responsible AI.mp4
    05:17
  • 5 -Business benefits of responsible AI.mp4
    03:33
  • 6 -Amazon Services and Tools for Responsible AI.mp4
    14:47
  • 7 -Responsible Considerations to Select a Model.mp4
    07:02
  • 8 -Responsible Preparation for Datasets.mp4
    04:08
  • 9 -Transparent and Explainable Models.mp4
    07:06
  • 10 -AWS tools for transparency and explainability.mp4
    04:14
  • 11 -Responsible AI Trade-Offs.mp4
    07:10
  • 12 -Principles of Human-Centered Design for Explainable AI.mp4
    05:37
  • 1 -Intro.mp4
    00:55
  • 2 -Strategic Guidance for Security, Governance, and Compliance.mp4
    05:40
  • 3 -Compliance Standards for AI Systems.mp4
    05:39
  • 4 -AWS Services for Governance and Compliance.mp4
    03:59
  • 5 -Data Governance Strategies.mp4
    03:40
  • 6 -Approaches for Implementing Governance Strategies.mp4
    06:14
  • 7 -Security and Privacy Considerations for AI Systems.mp4
    05:21
  • 8 -AWS Services and Features for Securing AI Systems.mp4
    07:27
  • 9 -Understanding Data and Model Lineage.mp4
    03:52
  • 10 -Best Practices for Secure Data Engineering.mp4
    04:26
  • 1 -Amazon Q Business INTRO.mp4
    04:45
  • 2 -Amazon Q Business PARTE 1.mp4
    04:17
  • 3 -Amazon Q Business PARTE 2.mp4
    06:26
  • 4 -Amazon Q Business PARTE 3.mp4
    04:05
  • 5 -Amazon Bedrock.mp4
    06:33
  • 6 -Amazon Rekognition.mp4
    06:42
  • 7 -Amazon SageMaker.mp4
    04:22
  • 8 -Amazon Augmented AI (Amazon A2I).mp4
    04:22
  • 9 -Amazon Comprehend.mp4
    07:55
  • 10 -Amazon Comprehend DEMO.mp4
    04:39
  • 11 -Amazon Kendra.mp4
    06:22
  • 12 -Amazon Fraud Detector.mp4
    15:30
  • 13 -Amazon Lex.mp4
    04:34
  • 14 -Amazon Polly.mp4
    04:47
  • 15 -Amazon Textract.mp4
    05:42
  • 16 -Amazon Transcribe.mp4
    02:44
  • 17 -Amazon Translate.mp4
    04:41
  • 18 -Amazon Personalize.mp4
    12:17
  • 1 -Exam-Style Questions.mp4
    04:00
  • 2 -Register for the Exam.mp4
    03:19
  • 3 -Apply a 50% discount on your certification exam.mp4
    01:39

    • Description

    Pass the AWS AI Practitioner AIF-C01 exam with this course by Jairo Pirona | Practice Exam included | All topics covered

    What You'll Learn?

    • 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...

    Who is this for?

  • Professionals seeking AWS Certified AI Practitioner certification.
  • AI students interested in AWS cloud.
  • Developers wanting to apply AI in business solutions.
  • Executives looking to understand the value of AI for their business.

    • What You Need to Know?

  • Basic computing and cloud skills.
  • Access to an AWS account for hands-on practice.
  • Interest in artificial intelligence and machine learning.
  • Desire to obtain the AWS Certified AI Practitioner certification

    • More details

    Description

    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?

    1. 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.

    2. Generative Artificial Intelligence
      You 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.

    3. Foundation Models and Fine-Tuning
      You 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.

    4. Responsible Artificial Intelligence
      You 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.

    5. Security, Compliance, and Governance for AI Solutions
      You 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.

    Who this course is for:

    • Professionals seeking AWS Certified AI Practitioner certification.
    • AI students interested in AWS cloud.
    • Developers wanting to apply AI in business solutions.
    • Executives looking to understand the value of AI for their business.

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    Amazon AWS

    Udemy
    Udemy
    View courses Udemy
    Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.
    • language english
    • Training sessions 98
    • duration 8:33:06
    • Release Date 2024/11/17

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