Pearson - Machine Learning Fundamentals With Amazon Sagemaker on AWS Free Download

Pearson – Machine Learning Fundamentals With Amazon Sagemaker on Aws-iLLiTERATE
English | Size: 10.31 GB
Category: Tutorial

5+ Hours of Video Instruction
Machine Learning Fundamentals with Amazon SageMaker on AWS LiveLessons teaches the fundamental concepts and taxonomy for machine learning and provides a high-level overview of the tools, languages, and libraries that Amazon SageMaker uses, including the AWS console, Jupyter Notebooks, languages, and interactive data analysis libraries.

Overview

Machine Learning Fundamentals with Amazon SageMaker on AWS LiveLessons teaches the fundamental concepts and taxonomy for machine learning. It provides specific scenarios, so the user can determine if ML would be beneficial. The course also provides a high-level overview of the tools, languages, and libraries that Amazon SageMaker uses, including the AWS console, Jupyter Notebooks, languages such as Python, and interactive data analysis libraries such as Pandas. This course will also discusses common algorithms and models used with ML and Amazon SageMaker, which will help determine the appropriate model to use in specific business scenarios.

Through this course the user will walk through Amazon SageMaker’s end-to-end workflow using practical and pragmatic business scenarios. You’ll see how you can benefit from the ability of a machine to make predictions on future data through hands-on labs and key concepts. AWS is continuing to make great strides to innovate their Artificial Intelligence and Machine Learning Platform. The concepts learned in this course will provide you with the foundation to build your own innovative systems on this dynamic platform.

Topics include:

Module 1: What is Amazon SageMaker?
Module 2: Fundamentals Machine Learning Concepts with Practical Applications
Module 3: Amazon SageMaker Supporting Tools and Technologies
Module 4: Data and Model Management with Amazon SageMaker
Module 5: Predictions and Deployment with Amazon SageMaker

About the Instructor

Asli Bilgin is an award-winning, cloud computing executive who has more than two decades of experience working for companies such as Dell, Microsoft, and Amazon. Her firm, Nokta Consulting, specializes in IT transformation and modernization leveraging disruptive technologies such as cloud computing, machine learning, and blockchain. At Amazon, Asli created, launched, and ran the global Software as a Service program. At Microsoft, she led the cloud and web strategy for 80 countries in the Middle East and Africa, based out of Dubai. Asli is a passionate advocate for the impact that technology can make on people’s lives. She was the architect behind the LEGO and Microsoft partnership effort for WomenBuild, a program to promote compute science as an art and science specifically for girls and women.

Skill Level

Beginner

Learn How To

Understand key machine learning concepts and taxonomy
Identify appropriate use cases and business scenarios than can benefit from Amazon SageMaker
Leverage the languages, libraries, and tools used in conjunction with Amazon SageMaker
Identify, prepare, and load data for analysis with Amazon SageMaker
Build and train datasets, train and fine tune models, create predictions and deploy these models to production using Amazon SageMaker
Use Amazon SageMaker with the AWS Console & SageMaker Dashboard
Couple conceptual knowledge with the hands-on experience to generate real time and batch predictions
Use common tools used in Machine Learning such as Jupyter Notebooks
Walk away with a preliminary understanding of the languages and libraries used with Machine Learning

Who Should Take This Course

Data scientist or developer who would like to build, train, and deploy machine learning models with speed and ease
Software programmers or data analysts who want to remain current on the latest AI/ML developments
Anyone wanting to learn the fundamentals of machine learning including concepts, taxonomy, workflow, and tools
Anyone wanting to accelerate your learning of Amazon SageMaker by learning principles combined with pragmatic experience

Course Requirements

An AWS Account, should you wish to follow along (AWS Free Tier can be used for 2 months.)
Ideally, have some experience with AWS, but not required
Ideally, you should have familiarity with software languages used with data analytics or software development preferred (for example, Python, R)

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Pearson – Machine Learning Fundamentals With Amazon Sagemaker on AWS

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