AWS Machine Learning Specialty Certification Guide

Overview

Syllabus 10

1. Machine Learning Fundamentals 55 minutes

   This module introduces the foundational concepts of machine learning, including the modeling life cycle, data splitting, and validation techniques. Learners will explore how to prepare and evaluate datasets, apply cross-validation, and understand the importance of shuffling data to prevent overfitt…

2. AWS Services for Data Storage 1 hour

   This module introduces the core AWS data storage services, including S3, EBS, and RDS, and demonstrates how to create and manage storage resources. Learners will explore access control, encryption, and best practices for securing and organizing data in the AWS cloud. Practical exercises guide you t…

3. AWS Services for Data Migration and Processing 50 minutes

   This module introduces key AWS services for migrating, storing, and processing data, including hands-on experience with AWS Glue, Kinesis Data Firehose, and DataSync. Learners will explore how to move data between storage solutions, transform data for analytics, and process large datasets using man…

4. Data Preparation and Transformation 1.5 hours

   This module guides learners through essential data preparation techniques, including transforming categorical and numerical features, handling outliers and unbalanced datasets, and processing text data for machine learning. You will explore practical methods such as encoding, normalization, standar…

5. Data Understanding and Visualization 30 minutes

   This module introduces the principles of effective data visualization and the importance of clear communication in presenting analytical findings. Learners will explore foundational techniques for understanding and visually representing data to ensure insights are accessible and impactful.

6. Applying Machine Learning Algorithms 1.5 hours

   This module guides learners through the practical application of key machine learning algorithms, including linear regression, classification, clustering, and dimensionality reduction. You will gain hands-on experience building models from scratch, evaluating their performance, and understanding es…

7. Evaluating and Optimizing Models 35 minutes

   This module guides learners through the process of assessing machine learning model performance using key evaluation metrics. You will explore how to interpret precision, recall, F1 score, and AUC, and learn strategies for optimizing models based on these metrics.

8. AWS Application Services for AI/ML 55 minutes

   This module introduces key AWS services for artificial intelligence and machine learning applications, including tools for text-to-speech, speech-to-text, natural language processing, translation, document extraction, and chatbot creation. Learners will discover how to leverage these managed servic…

9. Amazon SageMaker Modeling 1 hour

   This module guides learners through the practical aspects of building, training, and deploying machine learning models using Amazon SageMaker. You will explore data storage formats, select appropriate instance types, configure scalability, secure your environment, and leverage debugging tools to mo…

10. Model Deployment 35 minutes

    This module guides you through the process of configuring and deploying machine learning models using AWS services. You will learn how to set up event triggers and finalize deployment settings for Lambda functions, enabling automated and scalable model inference. By the end, you'll be equipped to o…

Advantages and disadvantages

Some points apply to every course of this kind; see how we rank.

Taught by

• Packt - Course Instructors