AWS Certified Machine Learning - Specialty (Video Course)
- By Milecia McGregor
- Published Apr 28, 2024 by Pearson IT Certification.
Online Video
- Your Price: $239.99
- List Price: $299.99
- About this video
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Description
- Copyright 2024
- Edition: 1st
- Online Video
- ISBN-10: 0-13-828314-1
- ISBN-13: 978-0-13-828314-8
4+ Hours of Video Instruction
Learn the techniques and approaches to successfully pass the AWS Certified Machine Learning - Specialty Exam with hands-on exercises.
Getting the AWS Certified Machine Learning - certification highlights your versatility as an ML engineer. Usually, ML engineers focus on handling data and building models, so if you know can use cloud tools, it makes you an even more valuable as an MLOps engineer. Youll be able to ingest your own data, get through the feature engineering process, train and evaluate models, and deploy them to where they will be consumed. This certification shows that you know how to do full-stack ML development.
In this series of videos, author Milecia McGregor shares a mix of slides and demonstrations in AWS, along with some examples in Visual Studio with Python. Its just what you need to learn to pass the exam. It includes an overview of concepts with hands-on work using AWS tools like Kinesis and EMR.
Skill Level:
- Intermediate
Learn How To:
- Learn effective tips and techniques for passing the AWS Certified Machine Learning - Specialty exam
- Identify and implement data ingestion solutions with Kinesis
- Evaluate ML models
- Deploy ML models with AWS tools
Course requirement:
- Prerequisites: Knowledge of how to use various AWS tools to deploy ML models into different environments
- Knowledge of.data engineering principles and model training and evaluatio
Who Should Take This Course:
Job titles: ML engineer, DevOps engineer, Aspiring ML engineer
About Pearson Video Training:
Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Prentice Hall, Sams, and Que Topics include: IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. Learn more about Pearson Video training at http://www.informit.com/video.
Video Lessons are available for download for offline viewing within the streaming format. Look for the green arrow in each lesson.
Sample Content
Table of Contents
Introduction
Lesson 1: Data Engineering
1.1 Create data repositories for machine learning
1.2 Identify and implement a data ingestion solution
1.3 Decide between ingestion tools
1.4 Identify and implement a data transformation solution
1.5 Get some practice: questions and exercises
Lesson 2: Exploratory Data Analysis
2.1 Sanitize and prepare data for modeling
2.2 Perform feature engineering
2.3 Analyze data for machine learning
2.4 Visualize data for machine learning
2.5 Get some practice: questions and exercises
Lesson 3: Training Models
3.1 Frame business problems as machine learning problems
3.2 Select the appropriate model for a machine learning problem
3.3 Understand the intuition behind the model
3.4 Train machine learning models
3.5 Choose compute option
3.6 Get some practice: questions and exercises
Lesson 4: Evaluating Models
4.1 Perform hyperparameter optimization
4.2 Use other methods for hyperparameter optimization
4.3 Evaluate machine learning models
4.4 Compare models with different metrics
4.5 Implement machine learning best practices
4.6 Get some practice: questions and exercises
Lesson 5: Machine Learning Implementation and Operations
5.1 Build machine learning solutions for production
5.2 Address scaling concerns
5.3 Recommend and implement the appropriate machine learning services
5.4 Apply basic AWS security practices to machine learning solutions
5.5 Deploy and operationalize machine learning solutions
5.6 Get some practice: questions and exercises
Summary