AWS Certified Machine Learning - Specialty: AWS Certified Machine Learning - Specialty

  • 17 Courses | 13h 52m 49s
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Discover how to build, train, tune, and deploy machine learning models using the AWS Cloud as you prepare for the AWS Certified Machine Learning – Specialty certification exam.

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

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    What Is Data Engineering?
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COURSES INCLUDED

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.
12 videos | 40m has Assessment available Badge
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.
12 videos | 27m has Assessment available Badge
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.
14 videos | 34m has Assessment available Badge
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.
12 videos | 40m has Assessment available Badge
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.
13 videos | 38m has Assessment available Badge
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.
12 videos | 34m has Assessment available Badge
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.
13 videos | 36m has Assessment available Badge
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.
12 videos | 34m