Domain 1: Fundamentals of AI and ML

• Core Concepts:
• Define and differentiate between fundamental AI terminologies, including artificial intelligence (AI), machine learning (ML), deep learning, neural networks, and natural language processing (NLP).
• Describe the similarities and differences among AI, ML, and deep learning.
• Explain the concepts of model training and inference in machine learning.
• Discuss the importance of addressing bias and fairness in AI/ML systems.
• Explain the architecture, function, and applications of large language models (LLMs).
• Data:
• Describe the different types of data used in AI models, including labeled and unlabeled data, and various data formats (tabular, time-series, image, text, structured, and unstructured).
• Machine Learning Techniques:
• Select appropriate ML techniques (e.g., regression, classification, clustering) for specific use cases.
• Applications:
• Identify real-world applications of AI, such as computer vision, NLP, speech recognition, recommendation systems, fraud detection, and forecasting.
• Evaluation:
• Understand model performance metrics (e.g., accuracy, AUC, F1 score) and relevant business metrics (e.g., cost per user, ROI) for evaluating ML models.
• Inference:
• Describe various types of inferencing (e.g., batch, real-time).

AWS Services

• AWS AI/ML Services:
• Describe the capabilities and applications of Amazon Rekognition.
• Describe the capabilities and applications of Amazon Textract.
• Describe the capabilities and applications of Amazon Comprehend.
• Explain how Amazon SageMaker is used to build, train, and deploy ML models.

By the end of this course, learners should be able to:

• Demonstrate a foundational understanding of AI and ML concepts.
• Articulate how these concepts are applied within the machine learning pipeline.
• Identify appropriate AWS services for specific AI/ML use cases.
• Understand the ethical considerations related to AI, including bias and fairness.

Artificial intelligence (AI) and machine learning (ML) represent pivotal technological advancements that are currently catalyzing widespread innovation. The curriculum of the AWS Certified AI Practitioner (AIF-C01): Fundamentals of AI and ML course is designed to equip learners with a comprehensive understanding of the foundational principles pertinent to the initial domain of the AWS AIP certification examination. Initially, the course investigates core theoretical constructs, encompassing deep learning methodologies, neural network architectures, natural language processing (NLP) techniques, the processes of model training and inference, the critical considerations of bias and fairness in AI systems, and the architecture and capabilities of large language models (LLMs). Subsequently, the curriculum elucidates the integration of these conceptual elements within the broader machine learning pipeline. Finally, the course provides an in-depth examination of pertinent Amazon Web Services (AWS) offerings, including Amazon Recognition, Amazon Extract, Amazon Comprehend, and Amazon SageMaker, thereby illustrating how AWS delivers managed services in the domains of AI and ML. Upon completion of this course, participants will have acquired the requisite skills and theoretical knowledge to confidently address the first domain of the AWS certification examination, demonstrating a robust understanding of both the theoretical underpinnings and practical applications of AI and ML within the AWS ecosystem.