This repository contains my hands-on lab work and solutions from the AWS Academy Machine Learning for Natural Language Processing [178432] course.
The course focuses on building a solid understanding of Natural Language Processing (NLP) concepts and applying them using AWS Machine Learning services to solve real-world text-based problems.
AWS Academy Machine Learning for Natural Language Processing introduces core NLP concepts — from text preprocessing and vectorization to advanced techniques like sentiment analysis, named entity recognition, topic modeling, and multilingual solutions — using AWS services such as Amazon Comprehend and Amazon SageMaker.
| Lab | Title | Description |
|---|---|---|
| 2.1 | Applying ML to an NLP Problem | Introduction to framing and solving an NLP problem using machine learning |
| 3.3 | Encoding and Vectorizing Text | Converting raw text into numerical representations for ML models |
| 4.1 | Implementing Sentiment Analysis | Building a model to classify text sentiment (positive/negative/neutral) |
| 5.2 | Working with Entities | Extracting and working with named entities from text |
| 6.2 | Implementing Topic Modeling with N... | Applying topic modeling techniques to uncover themes in text data |
| 6.3 | Implementing Topic Modeling | Further exploration of topic modeling approaches |
| 7.1 | Implementing a Multi-lingual Solution | Building NLP solutions that support multiple languages |
- Amazon SageMaker
- Amazon Comprehend
- Python (NLP libraries)
- AWS Academy Learning Environment
Each lab is provided as a standalone PDF containing step-by-step instructions, explanations, and implementation details covered during the hands-on exercises.
This repository serves as a personal record of coursework and a reference for anyone learning NLP concepts on AWS. It documents my learning journey through the AWS Academy ML for NLP curriculum.
This content is based on official AWS Academy course material and is shared here for personal learning and reference purposes only.
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