- eBook:AWS Certified Machine Learning Specialty 2020 Certification Guide: The definitive guide passing the MLS-C01 exam on the very first attempt
- Author:Somanath Nanda, Weslley Moura
- Data:April 9, 2021
- Pages:312 pages
- Format:PDF, ePUB
- Master the core machine learning algorithms along with AWS implementation
- Build model training and inference pipelines and deploy machine learning models to AWS cloud
- Learn all about the AWS services available for machine learning in order to pass the MLS-C01 exam
Book DescriptionThe AWS Machine Learning Specialty Certification exam tests your competency to perform machine learning (ML) on AWS infrastructure. This book covers the entire exam syllabus in depth using practical examples to help you with your real-world machine learning projects on AWS.
Starting with an introduction to machine learning on AWS, you'll learn the fundamentals of machine learning and explore important AWS services for artificial intelligence (AI). You'll then see how to prepare data for machine learning and discover different techniques for data manipulation and transformation for different types of variables. The book also covers the handling of missing data and outliers and takes you through various machine learning tasks such as classification, regression, clustering, forecasting, anomaly detection, text mining, and image processing, along with their specific ML algorithms, that you should know to pass the exam. Finally, you'll explore model evaluation, optimization, and deployment and get to grips with deploying models in a production environment and monitoring them.
By the end of the book, you'll have gained knowledge of all the key fields of machine learning and the solutions that AWS has released for each of them, along with the tools, methods, and techniques commonly used in each domain of AWS machine learning.
What you will learn
- Understand all four domains covered in the exam, types of questions, exam duration, and scoring
- Become well-versed with machine learning terminologies, methodologies, frameworks, and AWS Services for machine learning
- Get to grips with data preparation and using AWS services for batch and real-time data processing
- Explore the built-in machine learning algorithms in AWS and build and deploy your own models
- Evaluate machine learning models and tune hyperparameters
- Deploy machine learning models with the AWS infrastructure
Who This Book Is ForThis book is for professionals and students who want to take and pass the AWS Machine Learning Specialty exam or gain deeper knowledge of machine learning with a special focus on AWS. Familiarity with the basics of machine learning and AWS services is necessary.
1. Machine Learning Fundamentals
2. AWS Application Services for AI/ML
Section 2: Data Engineering and Exploratory Data Analysis
3. Data Preparation and Transformation
4. Understanding and Visualizing Data
5. AWS Services for Data Storing
6. AWS Services for Data Processing
Section 3:. Data Modeling
7. Applying Machine Learning Algorithms
8. Evaluating and Optimizing Models
9. Amazon SageMaker Modeling