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Machine Learning on AWS

  • Course Code: Data Science - Machine Learning on AWS
  • Course Dates: Contact us to schedule.
  • Course Category: AWS Duration: 4 Days Audience: This course is geared for those who wants to Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow.

Course Snapshot 

  • Course: Machine Learning on AWS 
  • Duration: 4 days 
  • Skill-level: Foundation-level Machine Learning on AWS skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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 

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

  • Build machine learning apps on Amazon Web Services (AWS) using SageMaker, Apache Spark, and TensorFlow 
  • Learn model optimization and understand how to scale your models using simple and secure APIs 
  • Develop, train, tune, and deploy neural network models to accelerate model performance in the cloud 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Manage AI workflows by using AWS cloud to deploy services that feed smart data products 
  • Use SageMaker services to create recommendation models 
  • Scale model training and deployment using Apache Spark on EMR 
  • Understand how to cluster big data through EMR and seamlessly integrate it with SageMaker 
  • Build deep learning models on AWS using TensorFlow and deploy them as services 
  • Enhance your apps by combining Apache Spark and Amazon SageMaker 

Audience & Pre-Requisites 

This course is geared for attendees with basic Linux and computing skills who wish to Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow. 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills, Microsoft azure and Machine Learning knowledge 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Getting Started with Machine Learning for AWS 
  • Getting Started with Machine Learning for AWS 
  • How AWS empowers data scientists 
  • Identifying candidate problems that can be solved using machine learning 
  • Machine learning project life cycle 
  • Deploying models 
  1. Classifying Twitter Feeds with Naive Bayes 
  • Classifying Twitter Feeds with Naive Bayes 
  • Classification algorithms 
  • Naive Bayes classifier 
  • Classifying text with language models 
  • Naive Bayes – pros and cons 
  1. Predicting House Value with Regression Algorithms 
  • Predicting House Value with Regression Algorithms 
  • Predicting the price of houses 
  • Understanding linear regression 
  • Evaluating regression models  
  • Implementing linear regression through scikit-learn 
  • Implementing linear regression through Apache Spark 
  • Implementing linear regression through SageMaker’s linear Learner 
  • Understanding logistic regression 
  • Pros and cons of linear models 
  1. Predicting User Behavior with Tree-Based Methods 
  • Predicting User Behavior with Tree-Based Methods 
  • Understanding decision trees 
  • Understanding random forest algorithms 
  • Understanding gradient boosting algorithms 
  • Predicting clicks on log streams 
  1. Customer Segmentation Using Clustering Algorithms 
  • Customer Segmentation Using Clustering Algorithms 
  • Understanding How Clustering Algorithms Work 
  • Clustering with Apache Spark on EMR 
  1. Analyzing Visitor Patterns to Make Recommendations 
  • Analyzing Visitor Patterns to Make Recommendations 
  • Making theme park attraction recommendations through Flickr data 
  • Collaborative filtering 
  • Finding recommendations through Apache Spark’s ALS 
  • Recommending attractions through SageMaker Factorization Machines 
  1. Implementing Deep Learning Algorithms 
  • Implementing Deep Learning Algorithms 
  • Understanding deep learning 
  • Applications of deep learning 
  • Understanding deep learning algorithms 
  • Understanding convolutional neural networks 
  1. Implementing Deep Learning with TensorFlow on AWS 
  • Implementing Deep Learning with TensorFlow on AWS 
  • About TensorFlow 
  • TensorFlow as a general machine learning library 
  • Training and serving the TensorFlow model through SageMaker 
  • Creating a custom neural net with TensorFlow  
  1. Image Classification and Detection with SageMaker 
  • Image Classification and Detection with SageMaker 
  • Introducing Amazon SageMaker for image classification 
  • Training a deep learning model using Amazon SageMaker 
  • Classifying images using Amazon SageMaker 
  1. Working with AWS Comprehend 
  • Working with AWS Comprehend 
  • Introducing Amazon Comprehend 
  • Accessing AmazonComprehend 
  • Named-entity recognition using Comprehend 
  • Sentiment analysis using Comprehend 
  • Text classification using Comprehend 
  1. Using AWS Rekognition 
  • Using AWS Rekognition 
  • Introducing Amazon Rekognition 
  • Implementing object and scene detection 
  • Implementing facial analysis 
  1. Building Conversational Interfaces Using AWS Lex 
  • Building Conversational Interfaces Using AWS Lex 
  • Introducing Amazon Lex 
  • Building a custom chatbot using Amazon Lex 
  1. Creating Clusters on AWS 
  • Creating Clusters on AWS 
  • Choosing your instance types 
  • Distributed deep learning 
  1. Optimizing Models in Spark and SageMaker 
  • Optimizing Models in Spark and SageMaker 
  • The importance of model optimization 
  • Automatic hyperparameter tuning 
  • Hyperparameter tuning in Apache Spark 
  • Hyperparameter tuning in SageMaker 
  1. Tuning Clusters for Machine Learning 
  • Tuning Clusters for Machine Learning 
  • Introduction to the EMR architecture 
  • Tuning EMR for different applications 
  • Managing data pipelines with Glue 
  1. Deploying Models Built in AWS 
  • Deploying Models Built in AWS 
  • SageMaker model deployment 
  • Apache Spark model deployment 

Student Materials: Each student will receive a Student Guide with course notes, code samples, software tutorials, diagrams and related reference materials and links (as applicable). Our courses also include step by step hands-on lab instructions and and solutions, clearly illustrated for users to complete hands-on work in class, and to revisit to review or refresh skills at any time. Students will also receive the project files (or code, if applicable) and solutions required for the hands-on work. 

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