AWS Certified AI Practitioner (AIF-C01): Fundamentals of AI and ML
Field | Description / Template |
|---|---|
Purpose | This course provides a solid foundation in Artificial Intelligence (AI) and Machine Learning (ML), focusing on core concepts, learning techniques, and the complete ML lifecycle. It helps learners understand how machines learn, how to evaluate models, and how to leverage AWS AI/ML services. The course is also aligned with the AWS Certified AI Practitioner (AIF-C01) exam, reinforcing concepts through exam tips and reviews. |
Audience | Beginners, students, non-technical professionals, early-career engineers, and AWS certification aspirants interested in AI/ML fundamentals. |
Role | AI Practitioner, Cloud Practitioner, Data Analyst, Business Analyst, Junior ML Engineer, Technical Consultant. |
Domain | AI/ML, Cloud Computing, Data Science |
Skill Level | Beginner |
Style | Concept-driven learning with practical examples, AWS service walkthroughs, light hands-on exposure (SageMaker), and exam-oriented preparation with review questions. |
Duration | 1 Day |
Related Technologies | Amazon SageMaker, Amazon Rekognition, Amazon Textract, Amazon Comprehend, Amazon Translate, Amazon Polly, Amazon Transcribe, Amazon Lex, Amazon Forecast, Amazon Kendra, Amazon Personalize |
Course Description
This course introduces learners to the fundamental concepts of Artificial Intelligence and Machine Learning within the AWS ecosystem. It starts with basic AI/ML principles, including how machines learn, types of learning approaches, and data used in building models.
Learners will explore the full machine learning pipeline, covering feature engineering, model training, evaluation metrics for classification and regression, and ML operations. The course also highlights a wide range of AWS managed AI/ML services across vision, language, speech, and recommendation use cases.
Additionally, learners will gain exposure to Amazon SageMaker, including data preparation, feature engineering, and deployment workflows through guided labs. The course reinforces learning with exam tips and question reviews aligned with the AWS Certified AI Practitioner exam.
Who is this course for
Beginners exploring AI and Machine Learning
Students and freshers entering data or cloud domains
Business professionals working with AI-driven solutions
AWS certification aspirants (AI Practitioner)
Developers or analysts transitioning into AI/ML roles
Course Objectives
By the end of this course, learners will be able to:
Understand core AI and ML concepts and terminology
Explain how machines learn and different types of ML approaches
Describe the end-to-end machine learning pipeline
Apply feature engineering concepts and understand parameters vs hyperparameters
Evaluate ML models using classification and regression metrics
Identify and use AWS AI/ML services for various real-world use cases
Understand Amazon SageMaker capabilities for building and deploying models
Prepare effectively for the AWS Certified AI Practitioner (AIF-C01) exam
Prerequisites
Basic understanding of computers and cloud concepts
No prior AI/ML experience required
Familiarity with AWS basics is helpful but not mandatory
Interest in data-driven technologies
Course outline
Section 1: Introducing Basic AI and ML Concepts
Introducing Basic AI and ML Concepts
How Do Machines Learn?
Different Ways Machines Learn
Types of Data in AI Models
Exam Tips
Section 2: The Machine Learning Pipeline
Exploring the Machine Learning Pipeline
What Is Feature Engineering?
Hyperparameters vs. Parameters
Metrics for Classification Models
Metrics for Regression Models
Fundamentals of ML Operations
Exam Tips
Section 3: AWS Managed AI/ML Services and Applications
Introducing AWS AI/ML Services
Vision: Amazon Rekognition
Vision: Amazon Textract
Language: Amazon Comprehend
Language: Amazon Translate
Speech: Amazon Polly
Speech: Amazon Transcribe
Chatbots: Amazon Lex
Forecasting: Amazon Forecast
Personal Assistants: Amazon Kendra
Recommendations: Amazon Personalize
Exam Tips
Section 4: Unpacking Amazon SageMaker Exam Question Review
Introducing Amazon SageMaker
Amazon SageMaker Data Wrangler
Amazon SageMaker Feature Store
Lab: Amazon SageMaker Console Walkthrough
Amazon SageMaker Deployments
Exam Tips
Section 5: Exam Question Review
Basic AI Concepts and Terminologies
The Machine Learning Pipeline
AWS Managed AI/ML Services and Applications
Unpacking Amazon SageMaker