Natural Language Processing: From Theory to Practice

This site contains many useful resources for those learning and practising natural language processing (NLP). Topics range from basic machine learning concepts to very specific tools used in NLP and advanced concepts used in deep learning and generative AI.

This collection of tutorials, how-to’s and in-depth blog articles has been categorised here for easy searchability.

Part 1: Foundations of Machine Learning

1. Basic Machine Learning Concepts

2. Intermediate Machine Learning Concepts

3. Implementing A self-Learning System

Part 2: Foundations of NLP

1. Introduction to NLP: Basic Concepts and Applications

2. Linguistics and NLP: Language Structure and Analysis

3. Text Preprocessing and Representation: Tokenization, Stemming, and Lemmatization

4. Language Modeling and Probability Theory: N-Gram Models and Beyond

Part 3: NLP Techniques and Applications

1. Sentiment Analysis: Analyzing and Classifying Opinions and Sentiments

2. Named Entity Recognition: Extracting and Categorizing Named Entities

3. Text Classification: Categorizing Texts into Predefined Categories

4. Machine Translation: Translating Texts between Different Languages

5. Question Answering: Answering Questions Automatically from Texts

6. Anomaly Detection: Finding Irregularity

7. Text Clustering: Unsupervised Learning in Practice

8. Text Similarity: Comparing and Searching In Text

9. Text Summarization: Extracting Meaning from Text

Part 4: Advanced Topics in NLP

1. Deep Learning Concepts: The Building Blocks Explained

2. Word Embeddings and Semantic Analysis: Distributed Representations of Words and Phrases

3. Deep Learning and NLP: Neural Networks for Language Processing

4. Language Generation and Style Transfer: Generating Texts with Specific Styles and Characteristics

5. Ethical and Social Issues in NLP: Bias, Fairness, Privacy, and Security

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