ODKAZ: https://www.skoleni-kurzy.eu/kurz-86066
Kurz: Exam Readiness: AWS Certified Machine Learning - Specialty
DataScript s.r.o.
The AWS Certified Machine Learning – Specialty exam validates a candidate s ability to design, implement, deploy, and maintain machine learning (ML) or deep learning (DL) solutions for given business problems. People with one to two years of experience developing, architecting, or running ML - DL workloads on the AWS cloud should join this workshop to lea> how to prepare to successfully pass the exam. The workshop explores the exam’s topic areas, shows how they relate to machine learning on A ...
Cena kurzu:
... 400 EUR / Kurz
... včetně DPH: 484 EUR / Kurz
Objednat - pro přihlášení na kurz/školení klikněte na zvolený termín školení a místo konání
... 400 EUR / Kurz
... včetně DPH: 484 EUR / Kurz
Objednat - pro přihlášení na kurz/školení klikněte na zvolený termín školení a místo konání
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Popis kurzu
Exam Readiness: AWS Certified Machine Learning - Specialty
Kurz je určen pro ...
Audience This course is intended for:
- Machine learning practitioners preparing to take the AWS Certified Machine Learning – Specialty exam
Lektoři kurzu
Lektoři z firmy: DataScript s.r.o.
[Kurz] Program kurzu (obsah přednášky/semináře/rekvalifikace/studia) ...
Goals This course is designed to teach you how to:
- Identify their strengths and weaknesses in each of the exam domains.
- Create a subsequent study plan to prepare for the exam.
- Describe the technical topics and concepts making up each of the exam domains.
- Summarize the logistics and mechanics of the certification exam and its questions.
- Identify effective test taking strategies that can be used to answer exam questions.
* This course is designed to teach you how to:
- Identify their strengths and weaknesses in each of the exam domains.
- Create a subsequent study plan to prepare for the exam.
- Describe the technical topics and concepts making up each of the exam domains.
- Summarize the logistics and mechanics of the certification exam and its questions.
- Identify effective test taking strategies that can be used to answer exam questions.
Prerequisites We recommend that attendees of this course to have:
- One or two years of hands-on experience developing, architecting, or running ML/deep learning workloads on the AWS cloud.
- Proficiency at expressing the intuition behind basic ML algorithms and performing basic hyperparameter optimization
- Understanding of complete ML pipeline and its components
- Experience with ML and deep learning frameworks
- Understanding and applying model training, deployment and operational best practices
Course outline Module 0: Course Introduction Module 1: Exam Overview and Test-taking Strategies
- Exam overview, logistics, scoring, and user interface
- Question mechanics and design
- Test-taking strategies
- Domain 1.1: Data Repositories for machine learning
- Domain 1.2: Identify and implement a data-ingestion solution
- Domain 1.3: Identify and implement a data-transformation solution
- Walkthrough of study questions
- Domain 1 quiz
- Domain 2.1: Sanitize and prepare data for modeling
- Domain 2.2: Perform featuring engineering
- Domain 2.3: Analyze and visualize data for ML
- Walkthrough of study questions
- Domain 2 quiz
- Domain 3.1: Frame business problems as machine learning (ML) problems
- Domain 3.2: Select the appropriate model(s) for a given ML problem
- Domain 3.3: Train ML models
- Domain 3.4 Perform hyperparameter optimization
- Domain 3.5 Evaluate ML models
- Walkthrough of study questions
- Domain 3 quiz
- Domain 4.1: Build ML solutions for performance, availability, scalability, resiliency, and fault tolerance
- Domain 4.2: Recommend and implement the appropriate ML services and features for a given problem
- Domain 4.3: Apply basic AWS security practices to ML solutions
- Domain 4.4: Deploy and operationalize ML solutions
- Walkthrough of study questions
- Domain 4 quiz
[Kurz] Cíl školení / poznámka ke kurzu...
Goals This course is designed to teach you how to:
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[Školení] Další popis kurzu (úroveň, minimální znalosti, informace o cenách kurzu) ...
Prerequisites We recommend that attendees of this course to have:
- One or two years of hands-on experience developing, architecting, or running ML/deep learning workloads on the AWS cloud.
- Proficiency at expressing the intuition behind basic ML algorithms and performing basic hyperparameter optimization
- Understanding of complete ML pipeline and its components
- Experience with ML and deep learning frameworks
- Understanding and applying model training, deployment and operational best practices
Course outline Module 0: Course Introduction Module 1: Exam Overview and Test-taking Strategies
- Exam overview, logistics, scoring, and user interface
- Question mechanics and design
- Test-taking strategies
- Domain 1.1: Data Repositories for machine learning
- Domain 1.2: Identify and implement a data-ingestion solution
- Domain 1.3: Identify and implement a data-transformation solution
- Walkthrough of study questions
- Domain 1 quiz
- Domain 2.1: Sanitize and prepare data for modeling
- Domain 2.2: Perform featuring engineering
- Domain 2.3: Analyze and visualize data for ML
- Walkthrough of study questions
- Domain 2 quiz
- Domain 3.1: Frame business problems as machine learning (ML) problems
- Domain 3.2: Select the appropriate model(s) for a given ML problem
- Domain 3.3: Train ML models
- Domain 3.4 Perform hyperparameter optimization
- Domain 3.5 Evaluate ML models
- Walkthrough of study questions
- Domain 3 quiz
- Domain 4.1: Build ML solutions for performance, availability, scalability, resiliency, and fault tolerance
- Domain 4.2: Recommend and implement the appropriate ML services and features for a given problem
- Domain 4.3: Apply basic AWS security practices to ML solutions
- Domain 4.4: Deploy and operationalize ML solutions
- Walkthrough of study questions
- Domain 4 quiz
ODKAZ: https://www.skoleni-kurzy.eu/kurz-86066
Poslední aktualizace: 2024-04-17 19:19:20
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