Course Overview
- Course Title: Certified Natural Language Processing (NLP)
- Instructor: Muhammad Shafiq (Data Scientist | AI & ML Engineer | Lecturer | Researcher)
- Target Audience:
- Beginners and intermediate learners in AI/ML
- Developers aiming to specialize in NLP
- Data scientists seeking to expand into language processing
- Professionals interested in AI-driven text analysis
- Prerequisites:
- Basic knowledge of Python programming
- Familiarity with machine learning fundamentals (recommended but not mandatory)
Curriculum Highlights
- Key Topics Covered:
- Introduction to Natural Language Processing (NLP) and its applications
- Text preprocessing (tokenization, stemming, lemmatization)
- Traditional NLP techniques (TF-IDF, Bag of Words, n-grams)
- Machine learning for NLP (Naive Bayes, SVM, decision trees)
- Deep learning for NLP (RNNs, LSTMs, GRUs)
- Transformer architecture and attention mechanisms
- Large Language Models (LLMs) like BERT and GPT
- Fine-tuning pre-trained models for specific tasks
- Sentiment analysis, text classification, and named entity recognition (NER)
- Question-answering systems and chatbot development
- Model deployment in real-world applications
- Key Skills Learned:
- Implementing NLP pipelines using NLTK, spaCy, and Hugging Face
- Building text classification models with scikit-learn
- Developing deep learning models with TensorFlow and PyTorch
- Fine-tuning pre-trained LLMs for custom tasks
- Deploying NLP models in production environments
- Designing end-to-end NLP projects for real-world use cases
Course Format
- Duration:
- 3 practice tests (self-assessment)
- Self-paced online course with lifetime access
- Format:
- On-demand video lectures
- Mobile and TV access
- Resources:
- Downloadable code notebooks and datasets
- Quizzes for knowledge reinforcement
- Hands-on projects with step-by-step guidance