Course Overview

In this Practical Data Science with Amazon Sagemaker certification training course, candidates learn to solve a real-world use case with Machine Learning (ML) using Amazon SageMaker. From analyzing and visualizing a dataset to preparing the data, and feature engineering, candidates taking up this Practical Data Science with Amazon Sagemaker course learn about the different stages of a typical data science process for Machine Learning.

By enrolling in this Practical Data Science with Amazon Sagemaker online course, candidates also learn about the practical aspects of model building, tuning, and deployment with Amazon SageMaker. Trainers use real-life use cases, group exercises, and hands-on labs to deliver engaging sessions on Amazon SageMaker.

After completing this course, students will be able to:

  • Prepare a dataset for training
  • Train and evaluate a Machine Learning model
  • Automatically tune a Machine Learning model
  • Prepare a Machine Learning model for production
  • Think critically about Machine Learning model results

Upcoming Batches

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Key Features

  • Our training modules have 50% - 60% hands-on lab sessions to encourage Thinking-Based Learning (TBL).
  • Interactive-rich virtual and face-to-face classroom teaching to inculcate Problem-Based Learning (PBL).
  • AWS certified instructor-led training and mentoring sessions to develop Competency-Based Learning (CBL).
  • Well-structured use-cases to simulate challenges encountered in a Real-World environment.
  • Being an authorized AWS Training Partner gives us an edge over competition.
  • Integrated teaching assistance and support through experts designed Learning Management System (LMS) and ExamReady platform.

Who Should Attend

  • Developers
  • Data Scientists

Prerequisites

We recommend that attendees of this Practical Data Science with Amazon Sagemaker course have:

  • Familiarity with Python programming language
  • Basic understanding of Machine Learning

Course Outline Download Course Outline

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

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

  • Business challenge: Customer churn
  • Review customer churn dataset

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

  • 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

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

  • 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

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

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

Certification

    • By earning Practical Data Science with Amazon SageMaker certification, you will show your future or current employer that you have knowledge of AWS Cloud concepts.
    • Practical Data Science with Amazon SageMaker certification can be used to learn skills of Data Science for Machine Learning
    • On successful completion of Practical Data Science with Amazon SageMaker certification training aspirants receive a Course Completion Certificate from us
    • By successfully clearing the Practical Data Science with Amazon SageMaker certification exams, aspirants earn AWS Certification

Course Fee

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Course ID: 8550

Course Price at

₹ 19900 + 18% GST

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