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Practical Data Science with Amazon SageMaker

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Explore SageMaker & Data Science with Practical Data Science With Amazon SageMaker.

Course description

In this course of Practical Data Science With Amazon SageMaker, you will discover how to utilize Amazon SageMaker to solve a practical use case using machine learning (ML) and get findings that can be put into practice. The phases of a typical data science workflow for machine learning are covered in this course, from dataset analysis and visualization through data preparation and feature engineering. Additionally, individuals will learn how to build, train, tune, and deploy models with Amazon SageMaker in a practical way. Customer retention analysis is a real-world use case for customer loyalty programs.

Activities

This course will include the following:

  • Live Presentations
  • Hands-On Labs
  • Group Exercises

Course Objectives

You’ll be covering the following things in this course:

  • Assemble a training dataset
  • Develop and assess a machine learning model
  • Automate the fine-tuning of a machine learning model
  • Create a machine learning model that can be used in production.
  • Examine the output of machine learning models critically.

Intended Audience

The Practical Data Science With Amazon SageMaker course is fit for you if you belong to either of the following group of individuals:

  • Data Scientists
  • Developers

Prerequisites

You’ll be required to have familiarity with the following:

  • Python language
  • Essential machine learning (ML)

Module Breakdown

Course outline

Module 1: Introduction to machine learning

  • Types of ML
  • Job Roles in ML
  • Steps in the ML pipeline

Module 2: Introduction to data prep and SageMaker

  • Training and test dataset defined
  • Introduction to SageMaker
  • Demonstration: SageMaker console
  • Demonstration: Launching a Jupyter notebook

Module 3: Problem formulation and dataset preparation

  • Business challenge: Customer churn
  • Review customer churn dataset

Module 4: Data analysis and visualization

  • Demonstration: Loading and visualizing your dataset
  • Exercise 1: Relating features to target variables
  • Exercise 2: Relationships between attributes
  • Demonstration: Cleaning the data

Module 5: Training and evaluating a model

  • Types of algorithms
  • XGBoost and SageMaker
  • Demonstration: Training the data
  • Exercise 3: Finishing the estimator definition
  • Exercise 4: Setting hyper parameters
  • Exercise 5: Deploying the model
  • Demonstration: hyper parameter tuning with SageMaker
  • Demonstration: Evaluating model performance

Module 6: Automatically tune a model

  • Automatic hyper parameter tuning with SageMaker
  • Exercises 6-9: Tuning jobs

Module 7: Deployment / production readiness

  • Deploying a model to an endpoint
  • A/B deployment for testing
  • Auto Scaling
  • Demonstration: Configure and test auto scaling
  • Demonstration: Check hyper parameter tuning job
  • Demonstration: AWS Auto Scaling
  • Exercise 10-11: Set up AWS Auto Scaling

Module 8: Relative cost of errors

  • Cost of various error types
  • Demo: Binary classification cutoff

Module 9: Amazon SageMaker architecture and features

  • Accessing Amazon SageMaker notebooks in a VPC
  • Amazon SageMaker batch transforms
  • Amazon SageMaker Ground Truth
  • Amazon SageMaker Neo

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Course Schedule

Course Name Date Register
No schedule found for this Course.

Course Overview

Duration 1D / 8 HRS
Modality ViLT/ Classroom
Data Sheet DOWNLOAD
Check Dates CLICK HERE
Price 15,000.00

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