Language Modeling: Predictive Text and Contextual Understanding in NLP
artificial-intelligence-ai.
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
Read Also About Historical Data Bias in AI: Identifying and Addressing Legacy Inequities
