Live Online Natural Language Processing (NLP) Course for Data Science

 

Duration: 6 Weeks | Total Time: 36 Hours

Format: Live online sessions using Google meet or MS Teams with hands-on coding, mini-projects, and a capstone project by an industry expert.
Target Audience: College Students, Professionals in Finance, HR, Marketing, Operations, Analysts, and Entrepreneurs
Tools Required: Laptop with internet
Trainer: Industry professional with hands on expertise

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

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

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

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

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

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

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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