Natural Language Processing Fundamentals
Chapter 1: NLP Concepts and Advanced Text Processing
The Natural Language Processing Pipeline
Advanced Tokenization Methods
Stemming and Lemmatization Compared
Handling Noise in Text Data
Advanced Stop Word Customization
Text Normalization Techniques
Hands-on Practical: Building Preprocessing Pipelines
Quiz for Chapter 1
Chapter 2: Feature Engineering for Text Representation
From Bag-of-Words to TF-IDF
Calculating TF-IDF Scores
Using N-grams to Capture Context
Introduction to Feature Hashing
Dimensionality Reduction for Text Features
Comparing Different Text Representation Methods
Hands-on Practical: Generating Text Features
Quiz for Chapter 2
Chapter 3: Supervised Learning for Text Classification
Classification Algorithms Review
Applying Classifiers to Text Data
Model Evaluation Metrics for Classification
Cross-Validation Strategies
Hyperparameter Tuning for Text Models
Addressing Imbalanced Datasets
Practice: Building a Text Classifier
Quiz for Chapter 3
Chapter 4: Understanding Sequential Data with Embeddings
Limitations of Frequency-Based Models
Introduction to Distributional Semantics
Word Embedding Concepts
Word2Vec: CBOW and Skip-gram Architectures
GloVe: Global Vectors for Word Representation
Visualizing Word Embeddings
Using Pre-trained Word Embedding Models
Hands-on Practical: Working with Word Embeddings
Quiz for Chapter 4
Chapter 5: Introduction to Sequence Models for NLP
The Need for Sequence Awareness
Recurrent Neural Network (RNN) Basics
Understanding the Vanishing Gradient Problem
Long Short-Term Memory (LSTM) Networks
Gated Recurrent Units (GRUs)
Applying Sequence Models to Text
Hands-on Practical: Building a Simple Sequence Model
Quiz for Chapter 5
Cross-Validation Strategies
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- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, and Jerome Friedman, 2009 (Springer) - This foundational textbook provides comprehensive coverage of cross-validation techniques, including K-Fold and stratified sampling, essential for understanding model evaluation.
- Cross-validation: evaluating estimator performance, scikit-learn developers, 2024 - Official documentation offering practical examples and best practices for K-Fold and Stratified K-Fold cross-validation, with important warnings about preventing data leakage in machine learning pipelines.
- Pattern Recognition and Machine Learning, Christopher M. Bishop, 2006 (Springer) - A widely respected textbook that covers the principles of model evaluation, including cross-validation, offering a complementary perspective to other foundational texts.