AWS Certified Machine Learning-Specialty (ML-S) and Practice Exam

Pearson presents AWS Certified Machine Learning-Specialty (ML-S) and Practice Exam.

AWS Certified Machine Learning-Specialty (ML-S) and Practice Exam

Course Description

This course from Pearson 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 sub lesson approach, with a mix of screencasting and headshot treatment. This Pearson course will cover: 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 Learni... Read More »

This course from Pearson 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 sub lesson approach, with a mix of screencasting and headshot treatment.

This Pearson course will cover:

  • 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.

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

The supporting code for this LiveLesson is located at: http://www.informit.com/content/images/9780135556511/Downloads/9780135556511-final_notebook_source_code.zip

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Course Details:

Target Audience

  • 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

Prerequisites

  • In order to take this course, you need one to two years of experience with AWS and six months using ML tools. Ideally, candidates will have already passed the AWS Cloud.
Certificate Info:

Type of Certification

Certificate of Completion

Format of Certification

Digital and Print

Professional Association/Affiliation

This certificate is issued by Pearson LearnIT

Method of Obtaining Certification

Upon successful completion of the course, participants will receive a certificate of completion.

Course Outline

  • AWS Certified Machine Learning Speicalty (ML-S) Certification
  • Learning objectives
  • 1.1 Get an overview of the certification
  • 1.2 Use exam study
  • 1.3 Review the exam guide
  • 1.4 Learn the exam strategy
  • 1.5 Learn the best practices of ML on AWS.
  • 1.6 Learn the techniques to accelerate hands-on practice
  • 1.7 Understand important ML related services
  • Learning objectives
  • 2.1 Learn data ingestion concepts
  • 2.2 Using data cleaning and preparation
  • 2.3 Learn data storage concepts
  • 2.4 Learn ETL solutions [Extract-Transform-Load]
  • 2.5 Understand data batch vs data streaming
  • 2.6 Understanding data security
  • 2.7 Learn data backup and recovery concepts
  • Learning objectives
  • 3.1 Understand data visualization: Overview
  • 3.2 Learn Clustering
  • 3.3 Use Summary Statistics
  • 3.4 Implement Heatmap
  • 3.5 Understand Principle Component Analysis (PCA)
  • 3.6 Understand data distribuitions
  • 3.7 Use data normalization techniques
  • Learning objectives
  • 4.1 Understand AWS ML Systems: Overview (Sagemaker, AWS ML, EMR, MXNet)
  • 4.2 Use Feature Engineering
  • 4.3 Train a Model
  • 4.4 Evaluate a Model
  • 4.5 Tune a Model
  • 4.6 Understand ML Inference
  • 4.7 Understand Deep Learning on AWS
  • Learning objectives
  • 5.1 Understand ML operations: Overview
  • 5.2 Use Concentration with Machine Learning and Deep Learning
  • 5.3 Implement continous deployment and deployment and delivery for Machine Learning
  • 5.4 Understand A/B Testing production deployment
  • 5.5 Troubleshoot production deployment
  • 5.6 Understand production security
  • 5.7 Understand cost and efficency of ML systems
  • Learning objectives
  • 6.1 Create Machine Learning Data Pipeline
  • 6.2 Perform Exploratory Data Analysis using AWS Sagemaker
  • 6.3 Create Machine Learning Model using AWS Sagemaker
  • 6.4 Deploy Machine Learning Model using AWS Sagemaker
  • Learning objectives
  • 7.1 Sagemaker Features
  • 7.2 DeepLense Features
  • 7.3 Kinesis Features
  • 7.4 AWS Flavored Python
  • 7.5 Cloud9
  • AWS Certified Machine Learning Speciality (ML-S): Summary

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