Learn how to make machines understand human language — emails, tweets, reviews, voice commands — and build powerful NLP applications from scratch.
Duration: 4–6 Months
Level: Beginner to Advanced
Prerequisites: Basic Python + Some knowledge of Machine Learning (recommended but not mandatory)
Module 1: NLP Fundamentals – What, Why & Where
Start from the absolute basics — even if you’re new to NLP.
Topics:
- What is NLP and why does it matter?
- NLP in everyday life: Siri, ChatGPT, Google Translate, Alexa
- NLP vs Machine Learning vs Deep Learning – What’s the difference?
- Core tasks in NLP: Text classification, translation, summarization, chatbots
- NLP career scope and industry applications
Module 2: Python & Text Handling for NLP
Learn how to prepare text data for AI in Python.
Topics:
- Working with strings in Python
- Text preprocessing: lowercase, punctuation, stopwords
- Tokenization, stemming & lemmatization
- Word frequency and n-grams
- Hands-on with NLTK, spaCy, and TextBlob
- Your first NLP project: Sentiment analysis on movie reviews
Module 3: Text Representation – Turning Words into Numbers
Machines don’t understand words — so we turn them into vectors.
Topics:
- Bag of Words model
- TF-IDF (Term Frequency – Inverse Document Frequency)
- Word embeddings: Word2Vec, GloVe
- Sentence embeddings (Sentence Transformers)
- Comparing methods: When to use what?
Module 4: Text Classification & Sentiment Analysis
Teach machines to classify and understand meaning in text.
Topics:
- Building text classifiers (spam detection, fake review detection)
- Sentiment analysis (positive/negative/neutral)
- Using Scikit-learn + Naive Bayes, SVM, and Logistic Regression
- Model evaluation: Precision, Recall, F1-score for NLP
- Real project: Twitter sentiment dashboard
Module 5: Sequence Models & Language Modeling
Learn how machines predict, complete, or generate sentences.
Topics:
- Introduction to sequences in NLP
- N-gram models & Markov Chains
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory (LSTM) networks
- Build a simple text generator from scratch
Module 6: Machine Translation, Summarization & Q&A Systems
Let AI translate, summarize, and answer like a human.
Topics:
- Sequence-to-sequence (Seq2Seq) models
- Text summarization: extractive vs abstractive
- Introduction to attention mechanism
- Neural machine translation basics (English to Hindi/Spanish etc.)
- Building a basic Q&A bot using Hugging Face Transformers
Module 7: Transformers, BERT & Modern NLP
The real power behind ChatGPT, Google BERT, and more.
Topics:
- What are Transformers in NLP? (Simple explanation)
- Intro to BERT, GPT, RoBERTa, T5
- Using Hugging Face library (transformers pipeline)
- Fine-tuning BERT for your own dataset
- Applications: Question answering, Named Entity Recognition, Zero-shot classification
Module 8: NLP Applications & Real-World Projects
Build job-ready applications using what you’ve learned.
Projects:
- Email Spam Classifier
- News Article Summarizer
- Multilingual Translation App
- Resume Screening using NLP
- Chatbot for Customer Support (using Rasa or ChatterBot)
Module 9: NLP in the Real World – Ethics, Bias & Challenges
With great power comes great responsibility.
Topics:
- Bias in language models
- Hate speech & offensive content detection
- Handling fake news and misinformation
- Explainable NLP: How to make AI decisions transparent
- Responsible use of NLP tools
Tools & Libraries You’ll Use
- Python
- NLTK, spaCy, TextBlob
- Scikit-learn
- TensorFlow / PyTorch
- Hugging Face Transformers
- Streamlit or Flask (for apps)
- Google Colab / Jupyter Notebooks
Capstone Project + Certification
One big project where you apply everything you’ve learned.
Ideas:
- Build a multilingual chatbot
- Create a resume parser + job matcher
- Develop a fake news detector
- Build a customer feedback analyzer for any brand
Present your project with a report, video, or live app and receive your NLP Master Diploma Certificate!
Bonus Module: Career + Freelance Launchpad
Ready to monetize your skills? Let’s help you get started.
Topics:
- Top NLP job roles and skills recruiters look for
- How to build an NLP portfolio on GitHub
- Freelancing platforms for NLP gigs
- Resume & LinkedIn for AI/NLP jobs
- Client proposal and pricing template (for freelancers)
