Training: AWS Certified Machine Learning – Specialty
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23 uur
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Training: AWS Certified Machine Learning – Specialty

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Machine learning (ML) is onmisbaar geworden in alle sectoren. Met duizelingwekkende hoeveelheden gegevens die wereldwijd elke seconde worden gegenereerd, is het onmogelijk om deze te begrijpen zonder dergelijke geavanceerde gegevensanalyses te gebruiken. De AWS Certified Machine Learning - Specialty-certificering is een van de meest begeerde, maar uitdagende certificaten die een data-ingenieur of wetenschapper kan behalen. Om te slagen voor het bijbehorende examen, moet je aantonen dat je kennis hebt van verschillende machine learning-concepten en dat je in staat bent om echte zakelijke uitdagingen op te lossen. Gebruik deze training om je voor te bereiden op het behalen van deze waardevolle certificering. Leer over de belangrijkste terminologie, concepten, tools, taken en workflows voor data-engineering en machine learning. Duik vervolgens in hoe het AWS Machine Learning-platform wordt gebruikt voor toepassingen in de echte wereld.

Na het afronden van deze training herken je de belangrijkste ML-concepten, weet je hoe je datasets voorbereidt, ML-modellen ontwikkelt en modellen optimaliseert voor verbeterde voorspellende nauwkeurigheid. Onderwerpen die o.a. aan bod komen, zijn data engineering, verkennende data analyse, modeling en de implementatie en uitvoering van machine learning.

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AWS Certified Machine Learning – Specialty

23 uur

AWS Certified Machine Learning: Data Engineering, Machine Learning, & AWS

  • Machine learning (ML) has become indispensable across all industries. With staggering amounts of data generated globally every second, it's impossible to make sense of it without using such advanced data analytics.
  • The AWS Certified Machine Learning - Specialty certification is one of the most coveted yet challenging certs a data engineer or scientist can get. To pass the associated exam, candidates must demonstrate knowledge of various machine learning concepts and the ability to solve real-world business challenges. Use this course to prepare for acquiring this valuable certification.
  • Get to grips with key data engineering and machine learning terminology, concepts, tools, tasks, and workflows. Then, dive into how the AWS Machine Learning platform is used for real-world applications.
  • Upon completing this course, you'll recognize key ML concepts and how to prepare datasets, develop ML models, and optimize models for improved predictive accuracy.

AWS Certified Machine Learning: Amazon S3 Simple Storage Service

  • Amazon Simple Storage Service (S3) is widely used for many machine learning applications. Using Amazon S3, you can quickly and easily run machine learning algorithms on large databases using remote machines.
  • In this course, you'll explore the various data formats Amazon S3 uses for machine learning pipelines. You'll then examine several Amazon S3 services in detail, looking at their use cases, workflows, and features.
  • You'll also learn about the vital Amazon S3 functionalities related to security and access management and data storage, archiving, and analytics.
  • When you've finished this course, you'll be able to outline how Amazon S3 is used for machine learning tasks, taking you one step closer to being fully prepared for the AWS Certified Machine Learning – Specialty exam.

AWS Certified Machine Learning: Data Movement

  • As the amount of data being collected has exploded, it has

  • become crucial for businesses to rapidly access, transform, and
  • analyze data. From the traditional batch processing to the
  • ever-evolving real-time data analytics, AWS has various tools to
  • handle large volumes of data and perform real-time analytics to
  • ensure high-service uptimes and personalize recommendations.
  • Explore various Amazon tools like AWS Glue, AWS Data Catalog, and
  • AWS Kinesis using this course. These tools are commonly used for
  • data movement. This course will also help you understand how these
  • processes function on the AWS platform and familiarize you with the
  • data movement workflows. Data movement and processing are at the
  • core of any data analysis, and after completing this course, you'll
  • be familiar with multiple tools and approaches that can be used to
  • conveniently transform raw data, combine databases, and stream
  • data, Further, you'll be able to prepare for the AWS Certified
  • Machine Learning - Specialty certification.

AWS Certified Machine Learning: Data Pipelines & Workflows

  • Creating a data pipeline is essential to making any data-related

  • product. AWS Data Pipeline, AWS Batch, and AWS Workflow frameworks
  • allow you to manage data using ETL data management across various
  • AWS tools and services, making AWS a perfect platform for combining
  • data from multiple sources. In this course, you'll learn how to
  • automate data movement and transformation processes on AWS and
  • define data-driven pipelines and workflows. Investigating how data
  • pipelines enable seamless, scalable, and fault-tolerant data
  • transfer between AWS storage and computational tools helps
  • illuminate the full potential of AWS in machine learning. By the
  • end of this course, you'll have a working knowledge of the most
  • common use cases of AWS Data Pipeline, AWS Batch, and AWS Workflow,
  • bringing you closer to being fully prepared for the AWS Certified
  • Machine Learning - Specialty certification exam.

AWS Certified Machine Learning: Jupyter Notebook & Python

  • Exploring and analyzing data to comprehend its underlying characteristics and patterns becomes increasingly vital as vaster amounts are collected. This is key in formulating the most suitable problems, the solving of which helps achieve real-world business goals.
  • Use this course to get your head around the programming fundamentals for machine learning in AWS, which form the basis for most data exploratory steps on the AWS platform.
  • Explore various Python packages used in machine learning and data analysis and become familiar with Jupyter Notebook's fundamental concepts. Then, work with Python and Jupyter Notebook to create a machine learning model.
  • When you're done, you'll be able to use Jupyter Notebook and various Python packages in machine learning and data analysis. You'll be one step closer to being prepared for the AWS Certified Machine Learning - Specialty certification exam.

AWS Certified Machine Learning: Data Analysis Fundamentals

  • Data Analysis is a primary method for deriving valuable insight

  • from raw and unstructured data. The appropriate application of data
  • analysis techniques is vital in deriving only the relevant insight
  • and factual knowledge from available data. Picking the correct data
  • distribution or visualization technique can become critical to the
  • overall data analysis results. Using this course, become familiar
  • with the core foundations of data – the essential ground for any
  • data analysis and machine learning operation. Examine the various
  • types of data that exist, inherent data distributions, both
  • traditional and modern methods of visualizing data, and how time
  • series analysis works. When you've completed this course, you'll be
  • able to describe the core concepts of data analysis and implement
  • some valuable visualization and analysis techniques using Python.
  • This course will prepare you for the AWS Certified Machine Learning
  • - Specialty certification exam.

AWS Certified Machine Learning: Athena, QuickSight, & EMR

  • Amazon offers a wide range of services that help enhance AWS workflows, making it much easier to create automated data processing and machine learning pipelines. Use this course to get to grips with some of these services.
  • Explore how Amazon Athena is used for querying data and how Amazon QuickSight integrates with Athena to help decision-makers analyze data and interpret information in an interactive visual environment. Then, get hands-on practice working with both tools.
  • Moving along, learn how Amazon EMR is used to process large amounts of data and investigate its integrations with various Apache frameworks, such as Hadoop and Spark.
  • When you're done, you'll know how to use Amazon services to automate machine learning processes, further preparing you for the AWS Certified Machine Learning – Specialty certification exam.

AWS Certified Machine Learning: Feature Engineering Overview

  • Feature engineering is key in extracting the right attributes from raw incoming data, which is fundamental in building reliable ML algorithms. Amazon SageMaker, a fully managed machine learning studio on AWS, provides feature engineering functionality and many other machine-learning-related tasks.
  • Use this course to explore fundamental feature engineering concepts and learn how to use Amazon SageMaker for feature engineering tasks. Work with the various tools available in SageMaker for preparing data for ML models, such as Ground Truth (for labeling data) and Feature Store (for storing, retrieving, and sharing features).
  • Moving along, investigate various deficiencies, such as missing values, imbalance, and outliers, in real-world data and learn how to address these challenges.
  • Upon completion, you'll be able to carry out feature engineering tasks efficiently using Amazon SageMaker, further preparing you for the AWS Certified Machine Learning – Specialty certification exam.

AWS Certified Machine Learning: Feature Engineering Techniques

  • Raw data is typically not perfect for developing effective

  • machine learning (ML) models. Often, it needs to be processed using
  • various feature engineering techniques to make it more suitable for
  • building accurate and optimized ML models. Take this course to
  • learn about techniques that help prepare the data to be compatible
  • and improve the performance of machine learning models. Investigate
  • techniques that are used to improve data usability, such as one-hot
  • encoding, binning, transformations, scaling, and shuffling. You
  • will also learn about the importance and usage of text feature
  • engineering and major workflows in the AWS environment. After
  • completing this course, you'll be able to implement feature
  • engineering techniques using AWS workflows, further preparing you
  • for the AWS Certified Machine Learning – Specialty certification
  • exam.

AWS Certified Machine Learning: Problem Framing & Algorithm Selection

  • Problem framing and algorithm selection is the most important

  • part of any machine learning (ML) project. ML engineers have to
  • apply appropriate techniques that will result in expected
  • prediction behavior. It is important to fully understand a
  • particular task and choose among all the available methods and
  • toolkits before implementing a machine learning project. Use this
  • course to learn more about the ML mindset, discover how
  • goal-oriented business problems can be formulated as machine
  • learning problems, and describe factors that drive the selection of
  • the correct algorithm for a particular scenario. The course will
  • also help you refresh important ML concepts and terminologies.
  • After completing this course, you'll be able to implement machine
  • learning solutions to solve business problems, further preparing
  • you for the AWS Certified Machine Learning – Specialty
  • certification exam.

AWS Certified Machine Learning: Machine Learning in SageMaker

  • Amazon SageMaker provides broad-set capabilities for machine

  • learning (ML) as it helps to prepare, train, and quickly deploy ML
  • models. Use this course to learn more about the basic capabilities
  • of SageMaker and work with it to implement solutions to various
  • machine learning problems. Explore features and functionalities of
  • SageMaker through practical demos and discover how to implement
  • hyperparameter tuning. This course will also help you explore
  • algorithms in SageMaker, such as linear learner, XGBoost, object
  • detection, and semantic segmentation. After completing this course,
  • you'll be able to train and tune a range of algorithms in order to
  • solve simple classification tasks for natural language processing
  • (NLP) and computer vision.

AWS Certified Machine Learning: ML Algorithms in SageMaker

  • Amazon SageMaker is a comprehensive machine learning (ML)
  • toolkit that provides a broad range of functions and can be used
  • for multiple use cases and tasks, making it an ultimate package for
  • ML. Dive deeper into SageMaker’s built-in algorithms for solving
  • problems, such as time series forecast, clustering, and anomaly
  • detection through this course. Examine various functionalities
  • available in Amazon SageMaker and learn how to implement different
  • ML algorithms. Once you have completed this course, you'll be able
  • to use SageMaker's machine learning algorithms for your business
  • case and be a step further in preparing for the AWS Certified
  • Machine Learning – Specialty certification exam.

AWS Certified Machine Learning: Advanced SageMaker Functionality

  • Amazon SageMaker can be used with multiple other frameworks and

  • toolkits to precisely define machine learning (ML) algorithms and
  • train models and is not limited to a specific set of algorithms for
  • ML. SageMaker also provides a wide range of tools that can be used
  • for incremental training, distributed training, debugging, or
  • explainability. Use this course to learn about advanced SageMaker
  • functionality, including supported frameworks, Amazon EMR, and
  • autoML. You'll also gain hands-on experience in using new features,
  • such as SageMaker Experiments, SageMaker Debugger, Bias Detection,
  • and Explainability. Once you have finished this course, you'll have
  • the skills and knowledge to implement SageMaker's advanced
  • functionalities. Further, you'll be a step closer to preparing for
  • the AWS Certified Machine Learning – Specialty certification
  • exam.

AWS Certified Machine Learning: AI/ML Services

  • Amazon offers a variety of high-level no-code services for

  • specialized machine learning (ML) tasks. These services are
  • primarily used to implement complex pre-built algorithms for using
  • ML with textual and visual information. Use this course to learn
  • more about these services. Use this course to explore services,
  • such as Amazon Kendra, Transcribe, Polly, Rekognition, Personalize,
  • and Textract in greater detail. You'll also delve into other AWS
  • AI/ML services through case studies. After you're done with this
  • course, you'll be able to describe the use cases of these services
  • and have a general overview of how to combine multiple AWS AI/ML
  • services to work within a single application. Moreover, you'll be a
  • step closer to preparing for the AWS Certified Machine Learning –
  • Specialty certification exam

AWS Certified Machine Learning: Problem Formulation & Data Collection

  • In order to build machine learning (ML) applications, it is

  • important to formulate problems and collect data. Examine the
  • choice between the online and on-premise implementation of the
  • problem formulation and data collection phases through this course.
  • Explore how SageMaker algorithms help complete ML projects
  • efficiently and work with various approaches that implement
  • recommender systems. You'll also investigate how and when to use
  • AWS data storage services and learn more about analyzing dataset
  • readiness. After taking this course, you'll be able to describe the
  • advantages and disadvantages of using the cloud over an on-premise
  • solution and define the problem formulation and success evaluation
  • processes. You'll also be a step closer to preparing for the AWS
  • Certified Machine Learning – Specialty certification exam.

AWS Certified Machine Learning: Data Preparation & SageMaker Security

  • Building successful machine learning (ML) applications require

  • the transformation of raw data, such that it meets the requirements
  • of individual ML algorithms. Explore how to prepare data using
  • Amazon SageMaker and S3 and create security services for this data
  • through this course. Start by delving deeper into summary
  • statistics and visualization before moving on to security best
  • practices for Amazon SageMaker. You'll also examine Amazon
  • CloudWatch and Amazon CloudTrail in greater detail. After taking
  • this course, you'll have a solid grasp of various data formats,
  • data security practices, and monitoring and alerting services used
  • in SageMaker. You'll also have the knowledge to prepare data for
  • machine learning and take a step further in your preparation for
  • the AWS Certified Machine Learning – Specialty certification
  • exam.

AWS Certified Machine Learning: Model Training & Evaluation

  • Training a machine learning (ML) model is the first step of many

  • when developing ML applications that enable businesses to discover
  • new trends within broad and diverse data sets. Use this course to
  • learn more about SageMaker's built-in algorithm and perform model
  • training, evaluation, monitoring, tuning, and deployment using
  • Amazon Elastic Compute Cloud (EC2) instances. Begin by examining
  • factorization machines and the selection of EC2 instances. Next,
  • you'll discover how to perform model training, evaluation, and
  • deployment. You'll wrap up the course by exploring the steps
  • involved in tuning and testing ML models. After you're done with
  • this course, you'll have the skills and knowledge to successfully
  • train and evaluate a model, further preparing you for the AWS
  • Certified Machine Learning – Specialty certification exam.

AWS Certified Machine Learning: AI Services & SageMaker Applications

  • Integrating AWS AI services and SageMaker with any machine

  • learning (ML) or deep learning project is a great way to enhance
  • its capabilities. Through this course, learn more about the
  • additional AWS AI Services that are ready to use in the form of
  • direct API without the need to train any ML models and dive deeper
  • into more SageMaker functionality. Get familiar with AWS AI
  • services that can be fully integrated into your applications in
  • minutes. This course will also introduce you to some pre-trained
  • algorithms in SageMaker for building high-performance natural
  • language processing (NLP) and computer vision apps using
  • fine-tuning techniques. After completing this course, you'll be
  • able to identify several AI services that can be used as APIs in
  • AWS and describe SageMaker's extensive capabilities in handling
  • text and images. You'll also be a step closer to preparing for the
  • AWS Certified Machine Learning – Specialty certification exam.

Kenmerken

Docent inbegrepen
Bereidt voor op officieel examen
Engels (US)
23 uur
Data Visualisatie
180 dagen online toegang
HBO

Meer informatie

Doelgroep Databasebeheerders, Data-analist
Voorkennis

Kennis op Associate-niveau van AWS-services, zoals bijvoorbeeld EC2.

Teven beschik je over voldoende basiskennis op het gebied van machine learning.
Resultaat

Na het afronden van deze training kun jij machine learning (ML)-oplossingen ontwerpen, implementeren, implementeren en onderhouden voor bepaalde zakelijke problemen met behulp van de AWS Cloud. Daarnaast ben jij optimaal voorbereid op het AWS Certified Machine Learning examen.

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