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:
-
Clase de calendario
Aprendizaje tradicional en el aula
-
Aprendizaje Virtual
Aprendizaje virtual
Solicitar este curso en un formato de entrega diferente.
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
Calendario
Parte superior-
- Método de Impartición: Aprendizaje Virtual
- Fecha: 30 mayo, 2024
- Sede: Aula Virtual
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- Método de Impartición: Aprendizaje Virtual
- Fecha: 12 septiembre, 2024
- Sede: Aula Virtual
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- Método de Impartición: Aprendizaje Virtual
- Fecha: 14 noviembre, 2024
- Sede: Aula Virtual
Dirigido a
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
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- GKAWS-MLB
- AWS DISCOVERY DAY: MACHINE LEARNING BASICS
- Cloud Computing
- GKAWS-MLB | AWS DISCOVERY DAY: MACHINE LEARNING BASICS | Training Course | Amazon Web Services.
- Amazon Web Services