📌 Overview: This course provides a deep dive into NLP, covering text processing, sentiment analysis, chatbots, transformers, and speech recognition. Learners will build hands-on projects, including chatbots and AI-powered text models.
📖 Module 1: Text Processing & Tokenization
🔹 Understanding Text Data (Words, Sentences, and Documents)
🔹 Tokenization (Word vs. Sentence Tokenization)
🔹 Stopwords Removal, Stemming, and Lemmatization
🔹 Part-of-Speech (POS) Tagging
🔹 Named Entity Recognition (NER)
🔹 Hands-on: Implement Tokenization & Text Preprocessing with NLTK/SpaCy
💬 Module 2: Sentiment Analysis & Chatbots
🔹 Introduction to Sentiment Analysis
🔹 Traditional Methods (Naive Bayes, Logistic Regression) vs. Deep Learning Approaches
🔹 Rule-Based vs. AI-Powered Chatbots
🔹 Intent Recognition & Dialog Management
🔹 Hands-on: Build a Sentiment Analysis Model & AI Chatbot with Rasa/Dialogflow
🤖 Module 3: Transformers & Large Language Models (LLMs)
🔹 Introduction to Transformers (Self-Attention Mechanism)
🔹 Understanding BERT, GPT, and T5
🔹 Fine-Tuning Pre-Trained Models for NLP Tasks
🔹 Zero-Shot, Few-Shot, and Transfer Learning in NLP
🔹 Hands-on: Fine-Tune a Transformer Model for Text Generation
🎙️ Module 4: Speech Recognition & AI Assistants
🔹 Basics of Automatic Speech Recognition (ASR)
🔹 Mel-Frequency Cepstral Coefficients (MFCCs) & Feature Extraction
🔹 Speech-to-Text Models (DeepSpeech, Whisper)
🔹 AI Voice Assistants (Google Assistant, Alexa, Siri)
🔹 Hands-on: Build a Speech Recognition Model & AI Assistant
🏆 Final Project: Build an End-to-End NLP System
🔹 Choose an NLP Task (Chatbot, Sentiment Analysis, Text Summarization, or Speech-to-Text)
🔹 Train & Optimize Your Model
🔹 Deploy the Model as a Web App
Instructor
