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Machine Learning on AWS – 4 Day Training Session

Course Details:

  • Class Start Date:

    Tuesday 8/15/2023

  • Class End Date:

    Friday 8/18/2023

  • Cost per Student:

    $ 1850

    • AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This course is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud. As you go through the lessons, you’ll gain insights into how these algorithms can be trained, tuned, and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the course will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every practical application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few lessons, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics and predictive modeling through to sentiment analysis. By the end of this course, you will be equipped with the skills you need to effectively handle machine learning projects and implement and evaluate algorithms on AWS. 

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Course Objectives

Working in a hands-on learning environment, led by our Machine Learning on AWS instructor, students will learn about and explore:

  • Building machine learning apps on Amazon Web Services (AWS) using SageMaker, Apache Spark, and TensorFlow
  • Model optimization and understand how to scale your models using simple and secure APIs
  • Developing, training, tuning, and deploying neural network models to accelerate model performance in the cloud
  • Managing AI workflows by using AWS cloud to deploy services that feed smart data products
  • Using SageMaker services to create recommendation models
  • Scaling model training and deployment using Apache Spark on EMR

Outline

Please see Modules below

Course Content

Machine Learning on AWS – 4 Day Training Session

Live Online Instructor Led Training
Tuesday 8.15.2023 – Friday 8.18.2023
08:30am EST – 4:30pm EST Daily
Cost per Student – $1850

AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This course is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud. As you go through the lessons, you’ll gain insights into how these algorithms can be trained, tuned, and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the course will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every practical application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few lessons, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics and predictive modeling through to sentiment analysis. By the end of this course, you will be equipped with the skills you need to effectively handle machine learning projects and implement and evaluate algorithms on AWS. 

Who should attend

  • This course is geared for those who want to gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow.
  • Skill-level: Foundation-level Machine Learning on AWS skills for Intermediate skilled team members. This is not a basic class.
  • IT Skills: Basic to Intermediate (1-5 years’ experience)
  • Microsoft Azure: Basic to Intermediate (1-5 years’ experience)
  • Machine Learning: Basic to Intermediate (1-5 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su

Module 1: Getting Started with Machine Learning for AWS

Module 2: Classifying Twitter Feeds with Naive Bayes

Module 3: Predicting House Value with Regression Algorithms

Module 4: Predicting User Behavior with Tree-Based Methods

Module 5: Customer Segmentation Using Clustering Algorithms

Module 6: Analyzing Visitor Patterns to Make Recommendations

Module 7: Implementing Deep Learning Algorithms

Module 8: Implementing Deep Learning with TensorFlow on AWS

Module 9: Image Classification and Detection with SageMaker

Module 10: Working with AWS Comprehend

Module 11: Using AWS Rekognition

Module 12: Building Conversational Interfaces Using AWS Lex

Module 13: Creating Clusters on AWS

Module 14: Optimizing Models in Spark and SageMaker

Module 15: Tuning Clusters for Machine Learning

Module 16: Deploying Models Built in AWS

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