Language Modeling: Predictive Text and Contextual Understanding in NLP

web-development.

Course Modules:

Module 1: What is a Language Model?

Definition and purpose of language modeling

Use cases: autocomplete, chatbots, translation, search

Generative vs. discriminative language models

Module 2: N-Gram Models and Probabilistic Foundations

Counting and calculating probabilities of word sequences

Smoothing techniques (Laplace, Kneser-Ney)

Limitations of fixed-window models

Module 3: Neural Language Models

From bag-of-words to neural networks

Word embeddings as input (Word2Vec, GloVe)

Recurrent Neural Networks (RNNs) and LSTMs for sequence modeling

Module 4: Transformer-Based Models

Self-attention and positional encoding

Intro to BERT, GPT, and large language models

Pretraining vs. fine-tuning for downstream tasks

Module 5: Applications of Language Modeling

Text generation and summarization

Sentiment analysis and classification

Semantic search, code completion, and Q&A

Module 6: Capstone Project – Build or Fine-Tune a Language Model

Choose: train a simple model or fine-tune a transformer

Use dataset (e.g., news, tweets, medical records)

Deliver a text generation demo or prediction tool with code

Tools & Technologies Used:

Python, TensorFlow or PyTorch

Hugging Face Transformers (BERT, GPT-2, DistilGPT)

NLTK, spaCy, Gensim

Google Colab / Jupyter Notebook

Target Audience:

Intermediate AI/NLP learners

Developers building intelligent writing or voice tools

Students and researchers exploring LLMs

Anyone interested in how ChatGPT and similar models work

Global Learning Benefits:

Understand the logic behind text prediction and generation

Apply real-world LLM tools to business or research

Bridge classical NLP and modern deep learning approaches

Build smarter applications powered by language understanding

?Master Study NLP Fundamentals: The Foundation of Language Understanding in AI

?Shop our library of over one million titles and learn anytime

?‍? Learn with our expert tutors