AWS DISCOVERY DAY: MACHINE LEARNING BASICS
- Código del Curso GKAWS-MLB
- Duración 1 Día
Otros Métodos de Impartición
Método de Impartición
Este curso está disponible en los siguientes formatos:
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Cerrado
Cerrado
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Clase de calendario
Aprendizaje tradicional en el aula
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Aprendizaje Virtual
Aprendizaje virtual
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Temario
Parte superiorLearn about important concepts, terminology, and the phases of a machine learning pipeline.
Are you interested in machine learning, but not sure where to start? Join us for this session with an AWS expert and demystify the basics. Using real-world examples, you’ll learn about important concepts, terminology, and the phases of a machine learning pipeline. Learn how you can unlock new insights and value for your business using machine learning.
- Level: Fundamental
- Duration: 1.5 hours
Company Events
These events can be delivered exclusively for your company at our locations or yours, specifically for your delegates and your needs. The Company Events can be tailored or standard course deliveries.
Calendario
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Parte superiorThis event is intended for:
- Developers
- Solution architects
- Data engineers
- Individuals interested in building solutions with machine learning - no machine learning experience required!
Objetivos del Curso
Parte superiorDuring this event, you will learn:
- What is Machine Learning?
- What is the machine learning pipeline, and what are its phases?
- What is the difference between supervised and unsupervised learning?
- What is reinforcement learning?
- What is deep learning?
Contenido
Parte superiorSection 1: Machine learning basics
- Classical programming vs. machine learning approach
- What is a model?
- Algorithm features, weights, and outputs
- Machine learning algorithm categories
- Supervised algorithms
- Unsupervised algorithms
- Reinforcement learning
Section 2: What is deep learning?
- How does deep learning work?
- How deep learning is different
Section 3: The Machine Learning Pipeline
- Overview
- Business problem
- Data collection and integration
- Data processing and visualization
- Feature engineering
- Model training and tuning
- Model evaluation
- Model deployment
Section 4: What are my next steps?
- Resources to continue learning
Siguientes Cursos Recomendados
Parte superiorCourses
- Deep Learning on AWS
- MLOps Engineering on AWS
- Practical Data Science with Amazon SageMaker
- The Machine Learning Pipeline on AWS
Resources
- AWS Ramp-Up Guide: Machine Learning