Number of Videos: 3.5 hours – 35 lessonsAuthor: John HeartyUser Level: Intermediate This course shows you how to use a range of AWS services to create intelligent end-to-end applications that incorporate ingestion, storage, preprocessing, machine learning (ML), and connectivity to an application client or server. The course is designed for data scientists looking for clear
Number of Videos: 3.5 hours – 35 lessons
Author: John Hearty
User Level: Intermediate
This course shows you how to use a range of AWS services to create intelligent end-to-end applications that incorporate ingestion, storage, preprocessing, machine learning (ML), and connectivity to an application client or server. The course is designed for data scientists looking for clear instruction on how to deploy locally developed ML applications to the AWS platform, and for developers who want to add machine learning capabilities to their applications using AWS services. Prerequisites include: Basic awareness of Amazon Simple Storage Service (S3), Elastic Compute Cloud (EC2), and Amazon Elastic MapReduce; as well as some knowledge of ML concepts like classification and regression analysis, model types, training and performance measures; and a general understanding of Python.
Understand how to use Amazon Web Service’s best-in-class streaming analytics and ML tools Learn about Amazon data pipelines: A very lightweight way to deploy an ML algorithm Explore Redshift and RDS: Databases that stage input data or store model outputs Discover Kinesis: A streaming data ingestion service that performs streaming analytical functions Learn to apply streaming and batch analytical processing to prepare datasets for ML algorithms Gain experience building ML models using Amazon Machine Learning and calling them using Python
John Hearty is a data scientist with Relic Entertainment who specializes in using Amazon Web Services to develop data infrastructure and analytics solutions. He is the author or co-author of three highly regarded books on machine learning (e.g., Packt Publishing’s “Advanced Machine Learning with Python”) and holds a Master’s degree in Computer Science from Liverpool John Moores University.
Mac Minimum System Requirements:Mac Recommended System Requirements:Processor: AnyRAM: AnyHard Disk: 1GBVideo Card: AnySupported OS: Mac OS Sierra 10.12, Mac El Capitan 10.11, Mac Yosemite 10.10, Mac Mavericks 10.9, Mac Mountain Lion 10.8, Mac Lion 10.7, Mac Snow Leopard 10.6, Mac Leopard 10.5, Mac OS X, Macintosh
