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
Introduction to Feature Hashing
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- Feature Hashing for Large Scale Multitask Learning, Kilian Weinberger, Anirban Dasgupta, John Langford, Alex Smola, Josh Attenberg, 2009 Proceedings of the 26th International Conference on Machine Learning (ICML) (Proceedings of Machine Learning Research (PMLR)) DOI: 10.48550/arXiv.0902.2206 - This seminal paper introduced and popularized the 'hashing trick' for efficient feature representation in machine learning, addressing scalability issues with high-dimensional data.
- Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, Jeff Ullman, 2014 (Cambridge University Press) - A comprehensive textbook on techniques for analyzing large-scale data, which includes discussions on hashing-based methods for dimensionality reduction, providing a broader context for feature hashing.
- sklearn.feature_extraction.FeatureHasher, scikit-learn developers, 2023 - The official documentation for scikit-learn's implementation of Feature Hashing, offering practical usage details, parameter explanations, and a concrete example of its application.