Natural Language using Python Programming for Beginners

🧱 Course Modules & Lessons

🔹 Module 1: Introduction to NLP

• What is Natural Language Processing?

• Real-world applications (chatbots, sentiment analysis, etc.)

• Overview of the NLP pipeline

• Python setup: Anaconda, Jupyter, Colab

• Installing NLTK, spaCy, and other libraries

🔹 Module 2: Text Preprocessing Basics

• What is tokenization?

• Removing punctuation, stopwords, and special characters

• Stemming vs Lemmatization

• Hands-on using NLTK and spaCy

• Project: Clean and prepare a news article dataset

🔹 Module 3: Text Representation Techniques

• Bag of Words (BoW)

• Term Frequency–Inverse Document Frequency (TF-IDF)

• CountVectorizer and TfidfVectorizer in Scikit-learn

• Word embeddings: intro to Word2Vec, GloVe

• Visualizing vectorized text with PCA

🔹 Module 4: Exploratory Text Analysis

• Word frequency analysis

• Word clouds and n-grams

• POS (Part-of-Speech) tagging

• Named Entity Recognition (NER)

• Project: Analyze text from Amazon or Yelp reviews

🔹 Module 5: Text Classification with Machine Learning

• Sentiment analysis using Logistic Regression

• Spam detection with Naive Bayes

• Splitting data, cross-validation, evaluation metrics

• Confusion matrix, precision, recall, F1-score

• Project: Build a movie review sentiment classifier

🔹 Module 6: Intermediate NLP Tasks

• Text summarization basics

• Topic modeling with LDA

• Language detection

• Intro to Regex for text processing

• Optional: Basic chatbots with rule-based logic

🔹 Module 7: Final Capstone Project

Choose one of the following:

• Sentiment analysis on tweets

• News categorization (sports, politics, tech)

• Resume parser using NER

• Build a simple chatbot using text patterns

Deliverables:

• Cleaned dataset

• Jupyter notebook with modeling

• Dashboard or visualization (optional)

• Presentation or GitHub portfolio upload