Data Engineering in AWS is the first course in the AWS Certified Machine Learning Specialty specialization. This course helps learners to analyze various data gathering techniques. They will also gain insight to handle missing data. This course is divided into two modules and each module is further segmented by Lessons and Video Lectures.
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This course facilitates learners with approximately 2:30-3:00 Hours Video lectures that provide both Theory and Hands -On knowledge. Also, Graded and Ungraded Quiz are provided with every module in order to test the ability of learners.
Module 1: Introduction to Data Engineering
Module 2: Feature extraction and feature selection
Course 1 of 5 in the Exam Prep MLS-C01: AWS Certified Specialty Machine Learning Specialization.
What You Will Learn
- Analyze various data gathering techniques
- Analyze techniques to handle missing data
- Implement feature extraction and feature selection with Principal Component Analysis and Variance Thresholds
Syllabus
WEEK 1
Introduction to Data Engineering
Welcome to Week 1 of Data Engineering in AWS Course. This week will begin with understanding SageMaker Jupyter Notebooks setup. We’ll also get an overview of handling and dropping Missing Data.This week will end by analyzing information about Gathering data.
WEEK 2
Feature extraction and feature selection
Welcome to Week 2 of Data Engineering in AWS Course. This week , we’ll learn to perform Feature extraction and feature selection with Principal Component Analysis and Variance Thresholds. We’ll also explore feature extraction and feature selection techniques. By the end of this week, we’ll analyze AWS Migration services and tools.
