This comprehensive guide prepares you for the AWS Certified Machine Learning Specialty (MLS-C01) exam. You'll gain expertise in designing machine learning solutions and deploying models on the AWS cloud.
Through detailed explanations of AWS services and machine learning concepts, you’ll build the knowledge necessary to succeed in real-world applications of machine learning. The course offers a practical approach to mastering AWS machine learning, covering everything from data preparation and transformation to model deployment using Amazon SageMaker.
You'll also dive into machine learning algorithms, optimization techniques, and the implementation of AI/ML services on AWS. What sets this course apart is its focus on exam preparation combined with professional skills development.
You’ll work through mock exams, self-assessment questions, and tips to ensure you're fully ready for the MLS-C01 certification, while also gaining hands-on experience with AWS services. This course is designed for students and professionals aiming to pass the AWS MLS-C01 exam or deepen their understanding of machine learning on AWS.
Prior knowledge of machine learning and AWS services is recommended to make the most of this content. This course is based on the book AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide, by Samanath Nanda, and Weslley Moura.
- Machine Learning Fundamentals
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 overfitting. By the end, participants will be equipped with essential skills for building and assessing machine learning models.
- AWS Services for Data Storage
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 through configuring storage and understanding the differences between storage types.
- AWS Services for Data Migration and Processing
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 managed AWS tools. By the end, you'll understand practical workflows for real-world data migration and processing scenarios.
- Data Preparation and Transformation
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, standardization, and TF-IDF to ensure your data is ready for modeling. By the end, you'll be equipped to address common data challenges and improve the quality of your machine learning pipelines.
- Data Understanding and Visualization
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.
- Applying Machine Learning Algorithms
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 essential concepts such as parsimony, stationarity, and cluster quality. By the end, you'll be equipped to select and implement appropriate algorithms for various data science tasks.
- Evaluating and Optimizing Models
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.
- AWS Application Services for AI/ML
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 services to solve real-world business challenges and automate complex workflows.
- Amazon SageMaker Modeling
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 monitor and optimize your models.
- Model Deployment
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 operationalize your models in real-world environments.
