Introduction
Natural Language Processing allows machines to understand, interpret, and generate human language. This course introduces the fundamentals behind NLP systems, breaking down techniques used for text classification, sentiment analysis, and language modeling. Participants will explore both classical NLP and modern deep learning–based approaches. Through practical exercises, learners will apply common NLP tools to real-world datasets. By the end, attendees will understand how modern NLP pipelines work from data preparation to model inference.
Course Objectives
- Learn basic text preprocessing and representation
- Understand key NLP algorithms and models
- Explore deep learning methods for NLP
- Use common NLP libraries and frameworks
- Build and evaluate NLP applications
Target Audience
- Students or professionals curious about NLP
- Developers and data scientists
- Business analysts exploring text analytics
- Educators and researchers
- Anyone working with large text datasets
Course Outline
- 5 Sections
- 0 Lessons
- 5 Days
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- Day 1: NLP Basics & Text Processing• Tokenization
• Stop-word removal
• Stemming vs. lemmatization
• N-grams
• Hands-on: Text preprocessing0 - Day 2: Classical NLP Approaches• Bag-of-Words
• TF-IDF
• Naive Bayes classification
• Language models
• Hands-on: Build a sentiment classifier0 - Day 3: Vector Representations• Word embeddings
• Word2Vec
• GloVe
• Semantic similarity
• Hands-on: Embedding experiments0 - Day 4: Deep Learning in NLP• RNNs and LSTMs
• Attention mechanisms
• Sequence-to-sequence models
• Transformers overview
• Hands-on: Train a small NLP model0 - Day 5: Applications & Trends• Chatbots and assistants
• Text summarization
• Language translation
• Ethics in NLP
• Capstone project0
Instructor
Lessons:
