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Data Pre-Processing
Data Pre-Processing
📌 Quiz Topic
📌 Quiz Topic
Lesson 1:
Lesson 1:
Key techniques for data cleaning and normalization.
Importance of removing duplicates and handling missing values.
Lesson 2:
Lesson 2:
Different tokenization methods and their use cases.
Best practices for preparing text data for embeddings.
Lesson 3:
Lesson 3:
Strategies for managing and processing large datasets.
Tools and libraries that enhance data processing efficiency.
Resources Provided Module 2
Resources Provided Module 2
- Sample Datasets:
Pre-populated SQLite database (employees.db).
Large employee dataset (employees_tokenized_large.csv) for Lesson 3.
- Python Scripts:
Starter scripts for data cleaning, tokenization, and large dataset processing.
Solution scripts demonstrating best practices and optimized approaches.
- AWS Setup Guides:
Step-by-step instructions for creating an AWS account and setting up S3 buckets.
- Udemy Courses:
Data Cleaning and Preprocessing in Python
Natural Language Processing with Python
Dask for Data Science
PySpark Complete Course
- Documentation and Tutorials:
Pandas Documentation on Data Cleaning
NLTK Tokenization Guide
spaCy Tokenization Documentation
HuggingFace Tokenizers
Boto3 Documentation
By completing Module 2, you’ll have a solid foundation in data pre-processing techniques essential for building scalable and efficient AI pipelines. The skills acquired here will prepare you for Module 3, where you will generate embeddings and work with vector databases to further enhance your data pipeline.
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