Natural Language Processing (NLP) with Python

📌 Course Overview

This comprehensive Natural Language Processing (NLP) with Python course is designed to take learners from beginner to advanced level, covering the complete NLP pipeline — from text preprocessing to deploying machine learning and deep learning models.

this course focuses on real-world NLP applications, industry-standard libraries, and end-to-end projects to make learners job-ready.

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🎯 What You Will Learn

• Complete Text Processing & Cleaning using Python
• Extract text from PDF, CSV, Excel, JSON, and audio files
• Use Regular Expressions (Regex) for advanced text search and preprocessing
• Perform NLP tasks using NLTK and spaCy
• Build Machine Learning & Deep Learning models for text classification
• Implement Topic Modeling, Sentiment Analysis, and Resume Parsing
• Deploy NLP models using Flask
• Publish your own Python package on PyPI

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🧠 Complete Course Curriculum

1️⃣ Working with Text & Data Files

• Reading and writing text files
• Handling CSV, Excel, JSON formats
• Text extraction from PDFs
• Introduction to audio file processing

2️⃣ Mastering Regular Expressions (Regex)

• Pattern matching and searching
• Text cleaning and extraction using regex
• Using Python re module effectively

3️⃣ spaCy for NLP

• Tokenization and POS tagging
• Named Entity Recognition (NER)
• Working with pre-trained spaCy models

4️⃣ NLTK for NLP

• Tokenization, stemming, lemmatization
• Named Entity Recognition and chunking
• Collocations and frequency analysis

5️⃣ Complete Text Cleaning Pipeline

• Removing URLs, hashtags, mentions, stopwords
• Expanding contractions
• Spelling correction
• WordCloud visualization
• Sentiment analysis using TextBlob

6️⃣ Build Your Own Text Processing Python Package

• Project structure and best practices
• Writing reusable text processing functions
• Using setup.py for package creation

7️⃣ Publish Python Package on PyPI

• Creating GitHub repositories
• Packaging and versioning
• Publishing and installing via pip

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🤖 Machine Learning for NLP

Linear Regression

• Theory and implementation
• Evaluation using Mean Squared Error (MSE)
• Interview-focused questions

Logistic Regression

• Binary classification
• Cost functions, entropy, overfitting
• Medical and real-world examples

SVM, KNN, Decision Tree & Random Forest

• Model training and tuning
• Performance evaluation
• Interview-oriented explanations

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📊 NLP Projects & Case Studies

Spam Text Classification

• Bag of Words (BoW)
• TF-IDF feature extraction
• Model training and evaluation

Sentiment Analysis (IMDB Reviews)

• TF-IDF + ML models (Logistic Regression, SVM)
• Model comparison and insights

Multi-Label Text Classification

• Stack Overflow tag prediction
• Multi-label classification techniques

Resume Parsing using spaCy

• Named Entity Recognition (NER)
• Automated resume information extraction

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🧠 Deep Learning for NLP

Word Embeddings

• Word2Vec and GloVe
• Using Gensim pretrained embeddings
• Similarity and analogy tasks

Deep Learning Models

• ANN and CNN for sentiment analysis
• Hate speech detection using CNN
• Disaster tweet classification

Text Generation

• Poetry generation using LSTM
• TensorFlow and Keras implementation

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🚀 Model Deployment

• Deploy ML/NLP models using Flask
• Build REST APIs for real-time predictions

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🧾 Course Requirements

• Desire to learn NLP and Machine Learning
• Basic understanding of Python
• Elementary-level mathematics

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👨‍🎓 Who This Course Is For

• Beginners in Natural Language Processing
• Data Scientists and Analysts
• Python Developers
• Students of Computer Science, AI, and Linguistics
• Professionals building chatbots, recommender systems, or NLP applications

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👨‍🏫 Instructor & Author

Uditya Narayan Tiwari B.Tech – Computer Science & Engineering (AI & ML)

• Experience in Machine Learning, NLP, Deep Learning
• Hands-on work in text classification, sentiment analysis, resume parsing, and ML model deployment

🔗 Portfolio: https://udityanarayantiwari.netlify.app/

🔗 GitHub: https://github.com/udityamerit

🔗 Knowledge Base: https://udityaknowledgebase.netlify.app/

🔗 LinkedIn: https://www.linkedin.com/in/uditya-narayan-tiwari-562332289/

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▶️ Repository Usage Instructions

1️⃣ Clone the Repository

git clone https://github.com/udityamerit/Natural-Language-Processing-Mastery-in-Python.git
cd Natural-Language-Processing-Mastery-in-Python

2️⃣ Create a Virtual Environment (Recommended)

python -m venv nlp-env
source nlp-env/bin/activate   # Linux / macOS
nlp-env\Scripts\activate      # Windows

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Launch Jupyter Notebook

jupyter notebook

5️⃣ How to Use This Repository

• Begin with 01 Working With Text Files to understand text handling fundamentals
• Continue with 02 Working With Regex for preprocessing and pattern matching
• Use test.ipynb to experiment and practice concepts

Each notebook is self-contained and designed for hands-on learning. Following the folder order is recommended for beginners.

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✅ Outcome

By the end of this course, learners will be able to design, build, evaluate, and deploy real-world NLP systems using traditional machine learning, deep learning, and modern NLP libraries. Learners will also gain the confidence to build industry-level NLP projects and open-source tools.

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