NLP Pre-Processing in Python | Text Cleaning | Batch 17 | AI & Data Science | Sir Nasir Hussain

Welcome to Batch 17 – NLP Pre-Processing Class of the AI & Data Science Course conducted at Saylani Z.A.I.T Park, led by Sir Nasir Hussain.
In this class, we focus on one of the most critical steps in Natural Language Processing (NLP) — Text Pre-Processing.

Before any Machine Learning or Deep Learning model can understand text, the raw data must be cleaned, structured, and converted into numerical form. This class builds a strong foundation for tasks like sentiment analysis, spam detection, chatbots, and text classification.

📘 What You Will Learn in This Class

What is NLP and why pre-processing is important

Understanding raw text vs processed text

Text cleaning techniques

Lowercasing, punctuation & noise removal

Tokenization explained step-by-step

Stopwords removal

Stemming vs Lemmatization (clear difference)

Handling special characters & numbers

Converting text into numerical form

Bag of Words (BoW) concept

TF-IDF explained with intuition

Preparing text data for ML models

🎯 Learning Outcomes

By the end of this class, you will:
✅ Understand the complete NLP pre-processing pipeline
✅ Be able to clean and prepare text datasets
✅ Convert text into ML-ready numerical features
✅ Build a strong base for NLP & AI projects

👨‍🏫 Course Details

Batch: 17

Topic: NLP Pre-Processing

Course: AI & Data Science

Instructor: Sir Nasir Hussain

Institute: Saylani Z.A.I.T Park

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