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
Addressing Imbalanced Datasets
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- SMOTE: Synthetic Minority Over-sampling Technique, Nitesh V. Chawla, Kevin W. Bowyer, Lawrence O. Hall, W. Philip Kegelmeyer, 2002 Journal of Artificial Intelligence Research, Vol. 16 (AI Access Foundation) DOI: 10.1613/jair.953 - Original research introducing the Synthetic Minority Over-sampling Technique (SMOTE) for addressing class imbalance.
- Imbalanced Learning: Foundations, Algorithms, and Applications, Haibo He, Yunqian Ma, 2013 (Wiley-IEEE Press) DOI: 10.1002/9781118646106 - A book offering a comprehensive overview of theories, algorithms, and applications related to learning from imbalanced data.
- imbalanced-learn: User Guide, The imbalanced-learn developers, 2024 - Official documentation for the Python library
imbalanced-learn, detailing various resampling methods like SMOTE, undersampling techniques, and pipeline integration. - User Guide: Imbalanced data, The imbalanced-learn developers, 2025 - The scikit-learn user guide section that discusses strategies for handling imbalanced datasets, including the
class_weightparameter and relevant evaluation metrics.