Fundamentals of Machine Learning and Artificial Intelligence (한국어)

Overview

In this course, you'll delve into the fundamentals of Machine Learning (ML) and Artificial Intelligence, examining the relationships among AI, ML, deep learning, and emerging generative AI. You'll solidify your understanding of essential AI terms, setting a foundation for deeper exploration. Additionally, you'll learn about how different AWS services utilize AI and ML capabilities to address real-world problems and drive innovation across various sectors.

• Course Level: Basic
• Duration: 1 Hour

Activities

The course includes interactive elements, videos, text guidance, and example graphics.

Course Objectives

Here’s what you'll learn:

• Identify similarities and differences among AI, ML, deep learning, and generative AI.
• Define fundamental AI and ML terms and concepts.
• Describe the types of data used in training AI models.
• Define supervised, unsupervised, and reinforcement learning (RL).
• Explain neural networks.
• Define basic generative AI terms and concepts.
• Explain the lifecycle of foundational models (FMs).
• Define the three key types of FMs: large language models, diffusion models, and multi-modal models.
• Understand AWS service capabilities and the benefits of utilizing AWS services for AI solutions.

Intended Audience

This course is designed for:

• Individuals interested in machine learning and artificial intelligence, irrespective of their specific job role.
• Individuals preparing for the AWS Certified AI Practitioner examination.

Prerequisites

It is recommended that you have:

• Basic understanding of AWS services.
• Basic knowledge of AI and ML concepts.

Course Outline

Section 1: Introduction

• How to use this course
• Introduction
  • Introductory video
  • Introduction to AI, ML, deep learning, and generative AI

Section 2: Basics of Artificial Intelligence

• Basics of Machine Learning
  • Training data
  • Machine learning process
  • Inference
• Basics of Deep Learning
  • Neural Networks
• Basics of Generative AI
  • Foundation Models
    • Large language models
    • Diffusion models
    • Multi-modal models
    • Other generative models
• Model output optimization
  • Prompt engineering
  • Fine-tuning
  • Retrieval-augmented generation
• Knowledge Check

Section 3: AWS Services

• AWS Infrastructure and Technology
  • AWS AI/ML Service Stack
    • ML Frameworks
    • AI/ML Services
    • Generative AI

      Syllabus

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      Taught by

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      Tags

      united states