Live Course Module: Natural Language Processing (NLP) Course for Data Science

Total Duration: 36 Hours (6 Weeks)

Prerequisite: Python programming, basic knowledge of Machine Learning

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Week 1: Introduction to NLP (6 Hours)

1. Overview of NLP and Applications — 1 hr

   • Understanding NLP in AI and Data Science

   • Real-world applications (Chatbots, Sentiment Analysis, Translation, etc.)

2. Text Preprocessing Basics — 2 hrs

   • Tokenization, Stopwords, Lemmatization, Stemming

   • Using NLTK and spaCy

3. Text Normalization Techniques — 1 hr

   • Lowercasing, punctuation removal, noise filtering

4. Bag of Words and TF-IDF — 2 hrs

   • Creating document-term matrices

   • Feature extraction using scikit-learn

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Week 2: Advanced Text Representation (6 Hours)

1. Word Embeddings Overview — 1 hr

   • Limitations of BoW, importance of contextual meaning

2. Word2Vec and GloVe — 2 hrs

   • Skip-gram vs CBOW

   • Implementing embeddings using Gensim

3. Sentence Embeddings and Document Vectors — 2 hrs

   • Sentence Transformers, Doc2Vec

4. Dimensionality Reduction for Text Data — 1 hr

   • PCA, t-SNE for word visualization

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Week 3: Text Classification Techniques (6 Hours)

1. Machine Learning for Text Classification — 2 hrs

   • Logistic Regression, Naive Bayes, SVM

2. Pipeline Building and Evaluation — 2 hrs

   • Cross-validation, confusion matrix, precision-recall

3. Project 1: Sentiment Analysis with Scikit-learn — 2 hrs

   • Twitter/IMDb review dataset

   • End-to-end model building

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Week 4: Deep Learning for NLP (6 Hours)

1. Neural Networks for NLP — 1 hr

   • Word embeddings + neural layers

2. Recurrent Neural Networks (RNN, LSTM, GRU) — 2 hrs

   • Sequential modeling, vanishing gradient issue

3. Text Generation and Sequence Models — 2 hrs

   • Character-level models, practical demo

4. Project 2: Text Classification using LSTM — 1 hr

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Week 5: Transformer Models & Modern NLP (6 Hours)

1. Introduction to Transformers — 2 hrs

   • Encoder-decoder architecture, self-attention mechanism

2. Understanding BERT, GPT, and Other Models — 2 hrs

   • Fine-tuning pre-trained models for NLP tasks

3. Hands-on: Text Classification using BERT — 2 hrs

   • Using Hugging Face Transformers library

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Week 6: NLP Applications & Capstone Project (6 Hours)

1. NLP in Real-World Systems — 1 hr

   • Chatbots, Recommendation Engines, Search Systems

2. Named Entity Recognition (NER) & Topic Modeling — 2 hrs

   • spaCy NER, Latent Dirichlet Allocation (LDA)

3. Capstone Project: End-to-End NLP Solution — 3 hrs

   • Example: “Customer Feedback Analysis System”

   • Data cleaning → Feature extraction → Model building → Deployment

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🎯 Course Outcomes

By the end of this course, learners will be able to:

• Preprocess and clean textual data efficiently.

• Apply both statistical and deep learning models for NLP tasks.

• Implement word embeddings and transformer-based models.

• Build end-to-end NLP projects for data science applications.

• Use popular NLP libraries: NLTK, spaCy, scikit-learn, Gensim, TensorFlow, PyTorch, Hugging Face.

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