AWS Certified Machine Learning-Specialty (ML-S) | O’Reilly


AWS Certified Machine Learning-Specialty (ML-S) | O’Reilly
English | Size: 9.70 GB
Genre: eLearning

More Than 7 Hours of Video Instruction

Overview

This course covers the essentials of Machine Learning on AWS and prepares a candidate to sit for the AWS Machine Learning-Specialty (ML-S) Certification exam. Four main categories are covered: Data Engineering, EDA (Exploratory Data Analysis), Modeling, and Operations. Description This 7+ hour Complete Video Course is fully geared toward the AWS Machine Learning-Specialty (ML-S) Certification exam. The course offers a modular lesson and sublesson approach, with a mix of screencasting and headhsot treatment.
Data Engineering instruction covers the ingestion, cleaning, and maintenance of data on AWS.
Exploratory Data Analysis covers topics including data visualization, descriptive statistics, and dimension reduction and includes information on relevant AWS services.
Machine Learning Modeling covers topics including feature engineering, performance metrics, overfitting, and algorithm selection.
Operations covers deploying models, A/B testing, using AI services versus training your own model, and proper cost utilization.

The supporting code for this LiveLesson is located at

http://www.informit.com/store/aws-certified-machine-learning-specialty-ml-s-complete-9780135556511.
What You Will Learn

How to perform data engineering tasks on AWS
How to use Exploratory Data Analysis (EDA) to solve machine learning problems on AWS
How to perform machine learning modeling tasks on the AWS platform
How to operationalize machine learning models and deploy them to production on the AWS platform
How to think about the AWS Machine Learning-Specialty (ML-S) Certification exam to optimize for the best outcome

Who Should Take This Course

DevOps engineers who want to understand how to operationalize ML workloads
Software engineers who want to ensure they have a mastery of machine learning terminology and practice on AWS
Machine learning engineers who want to solidify their knowledge about AWS machine learning practices
Product managers who need to understand the AWS machine learning lifecycle
Data scientists who run machine learning workloads on AWS

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