Exam Readiness: AWS Certified Machine Learning - Specialty
Cena kurzu:
... bez DPH: 400 EUR/Kurz
... včetně DPH: 484 EUR/Kurz
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
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Exam Readiness: AWS Certified Machine Learning - Specialty
Téma kurzu: Operační systémy, ,
Určeno pro ...Audience This course is intended for: - Machine learning practitioners preparing to take the AWS Certified Machine Learning – Specialty exam
Přednáší ...Lektoři z firmy: DataScript s.r.o. Program kurzu, obsah 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
Module 2: Domain 1: Data Engineering - 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
Module 3: Domain 2: Exploratory Data Analysis - 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
Module 4: Domain 3: Modeling - 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
Module 5: ML Implementation and Operations - 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
Module 6: Comprehensive study questions Module 7: Study Material Module 8: Wrap-up Další popis (úroveň, podmínky účasti, minimální znalosti) ...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
Module 2: Domain 1: Data Engineering - 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
Module 3: Domain 2: Exploratory Data Analysis - 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
Module 4: Domain 3: Modeling - 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
Module 5: ML Implementation and Operations - 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
Module 6: Comprehensive study questions Module 7: Study Material Module 8: Wrap-up Cíl školení / poznámka ke kurzuGoals 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.
Informace o rekvalifikaci Tento kurz není rekvalifikačníExam Readiness: AWS Certified Machine Learning - Specialty
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Akreditace - Certifikát - Zkouška
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