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👉 Welcome to Module 2: Data Processing

Clean, format, and prepare raw data for downstream tasks, ensuring high-quality inputs for embedding generation and further analysis.

Module Overview

Objective: In Module 2, you'll delve into the essential steps of data pre-processing. This involves transforming raw data into a clean and structured format, making it suitable for machine learning models and other downstream applications. You'll learn techniques for text cleaning, normalization, tokenization, and managing large datasets efficiently. Through hands-on activities and practical examples from the Jarvid project, you'll gain the skills necessary to ensure your data pipeline is robust and reliable.

Lesson Structure

Each lesson within this module includes:

1. Introduction Video (Pre-recorded Lecture): Detailed explanations of key concepts.

2. Hands-On Coding Session: Guided walkthroughs with downloadable resources.

3. Activities & Assignments: Practical exercises to apply your knowledge.

4. Quiz: Short assessments to test your understanding.

5. Resources: Links to external documentation, Udemy courses, and additional learning materials for deeper exploration.

What You´ll Learn in Module 2

Lesson 1: Text Cleaning and Normalization

• • Removing duplicates and irrelevant data.

  • Handling special characters and formatting inconsistencies.

  • Standardizing text for uniformity.

Lesson 2: Tokenization

• • Splitting text into meaningful units (tokens) for embedding.

  • Understanding different tokenization techniques.

  • Preparing tokens for embedding generation.

Lesson 3: Managing Large Datasets for Efficient Processing

• • Techniques for handling and processing large datasets.

  • Optimizing data storage and access for performance.

  • Utilizing efficient data structures and libraries.