📝 Natural Language Processing with ML

Master text processing, language understanding, and NLP applications using machine learning

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NLP with ML Curriculum

12
Core Units
~90
NLP Concepts
25+
Techniques
40+
Practical Examples
1

Introduction to NLP

Understand natural language processing fundamentals and the challenges of working with text data.

  • What is Natural Language Processing
  • NLP vs Computational Linguistics
  • Challenges in NLP
  • Applications and use cases
  • NLP pipeline overview
  • Text as data
  • Historical evolution of NLP
  • Current state and trends
2

Text Preprocessing & Tokenization

Learn essential techniques for cleaning and preparing text data for machine learning.

  • Text cleaning and normalization
  • Tokenization techniques
  • Sentence segmentation
  • Handling special characters
  • Case normalization
  • Unicode and encoding issues
  • Regular expressions for text
  • Language-specific preprocessing
3

Morphology & Part-of-Speech Tagging

Explore word structure, morphological analysis, and grammatical tagging.

  • Morphological analysis
  • Stemming algorithms
  • Lemmatization techniques
  • Part-of-speech tagging
  • POS tag sets
  • Rule-based vs statistical tagging
  • Hidden Markov Models for POS
  • Evaluation metrics
4

N-grams & Language Models

Master statistical language modeling using n-gram approaches and probability.

  • N-gram models
  • Unigram, bigram, trigram
  • Maximum likelihood estimation
  • Smoothing techniques
  • Laplace and Good-Turing smoothing
  • Backoff and interpolation
  • Perplexity evaluation
  • Language model applications
5

Text Representation & Vectorization

Learn how to convert text into numerical representations for machine learning algorithms.

  • Bag of Words (BoW)
  • Term Frequency (TF)
  • TF-IDF weighting
  • Document-term matrices
  • Sparse representations
  • N-gram features
  • Character-level features
  • Feature selection for text
6

Word Embeddings

Understand dense vector representations of words and their semantic properties.

  • Distributed representations
  • Word2Vec architecture
  • CBOW vs Skip-gram
  • GloVe embeddings
  • FastText extensions
  • Embedding evaluation
  • Semantic similarity
  • Analogy tasks
7

Text Classification

Apply machine learning algorithms to classify documents and text snippets.

  • Text classification pipeline
  • Feature engineering for text
  • Naive Bayes for text
  • SVM for text classification
  • Logistic regression
  • Multi-class classification
  • Evaluation metrics
  • Handling imbalanced data
8

Sentiment Analysis

Learn to detect and analyze emotions, opinions, and sentiments in text.

  • Sentiment analysis overview
  • Polarity classification
  • Emotion detection
  • Lexicon-based approaches
  • Machine learning approaches
  • Aspect-based sentiment
  • Handling negation
  • Domain adaptation
9

Named Entity Recognition

Identify and classify named entities like persons, organizations, and locations in text.

  • NER task definition
  • Entity types and schemas
  • BIO tagging scheme
  • Rule-based approaches
  • Machine learning for NER
  • CRF for sequence labeling
  • Feature engineering
  • Evaluation and metrics
10

Information Extraction

Extract structured information and relationships from unstructured text.

  • Information extraction overview
  • Relation extraction
  • Template filling
  • Pattern-based extraction
  • Machine learning approaches
  • Distant supervision
  • Knowledge base construction
  • Evaluation methodologies
11

Topic Modeling

Discover hidden thematic structures in large collections of documents.

  • Topic modeling concepts
  • Latent Dirichlet Allocation
  • Probabilistic topic models
  • LDA parameter estimation
  • Model selection and tuning
  • Topic coherence measures
  • Alternative topic models
  • Applications and visualization
12

NLP Applications & Systems

Build real-world NLP applications and understand deployment considerations.

  • Chatbots and dialogue systems
  • Question answering systems
  • Text summarization
  • Machine translation basics
  • Search and information retrieval
  • NLP system architecture
  • Performance optimization
  • Deployment and monitoring

Unit 1: Introduction to NLP

Understand natural language processing fundamentals and the challenges of working with text data.

What is Natural Language Processing

Learn the fundamental definition and scope of NLP as a field bridging computer science and linguistics.

AI Linguistics Text Analysis
Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and human language. It involves developing algorithms and models to understand, interpret, and generate human language in a valuable way.
# NLP involves multiple aspects
nlp_components = {
  "Understanding": "Comprehend meaning from text",
  "Generation": "Produce human-like text",
  "Translation": "Convert between languages",
  "Extraction": "Pull out specific information",
  "Classification": "Categorize text content",
  "Summarization": "Condense long texts"
}

# Example NLP task
text = "I love this movie! It's fantastic."
# NLP can determine: sentiment=positive, emotion=joy

NLP vs Computational Linguistics

Understand the relationship and differences between NLP and computational linguistics.

Computational Linguistics: Theory-focused, studies language as a computational system
NLP: Application-focused, builds practical systems for language tasks
Overlap: Both use computational methods for language processing
Computational Linguistics might ask: "How can we model the syntax of English using formal grammars?"

NLP might ask: "How can we build a system that accurately translates English to Spanish for business documents?"

Challenges in NLP

Explore the unique difficulties that make natural language processing complex and challenging.

Key NLP challenges: Ambiguity (multiple meanings), Context dependency, Variability in expression, Implicit knowledge requirements, Cultural and domain-specific language, and Real-world knowledge integration.
# Examples of NLP challenges

# 1. Ambiguity
sentence1 = "I saw a man with a telescope"
# Who has the telescope? The observer or the man?

# 2. Context dependency
sentence2 = "The bank is closed"
# Financial institution or river bank?

# 3. Sarcasm and irony
sentence3 = "Great! Another meeting..."
# Positive words, negative sentiment

# 4. Cultural references
sentence4 = "It's raining cats and dogs"
# Idiom meaning heavy rain, not literal animals

Applications and Use Cases

Discover the wide range of real-world applications where NLP creates value.

Search Translation Chatbots Analytics
# Major NLP applications
applications = {
  "Search Engines": {
    "query_understanding": "What user wants",
    "document_ranking": "Relevance scoring",
    "snippet_generation": "Result summaries"
  },
  "Virtual Assistants": {
    "speech_recognition": "Voice to text",
    "intent_detection": "What user wants",
    "response_generation": "Natural replies"
  },
  "Business Intelligence": {
    "sentiment_analysis": "Customer opinions",
    "trend_detection": "Topic monitoring",
    "document_analysis": "Contract review"
  }
}

NLP Pipeline Overview

Understand the typical stages involved in processing natural language data.

Text Input → Preprocessing → Tokenization → Linguistic Analysis → Feature Extraction → Machine Learning → Application Output
import nltk
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB

# Complete NLP pipeline example
def nlp_pipeline(text):
  # 1. Preprocessing
  text = text.lower().strip()
  
  # 2. Tokenization
  tokens = nltk.word_tokenize(text)
  
  # 3. Remove stopwords
  stop_words = set(nltk.corpus.stopwords.words('english'))
  filtered_tokens = [w for w in tokens if w not in stop_words]
  
  # 4. Rejoin for vectorization
  processed_text = ' '.join(filtered_tokens)
  
  return processed_text

# Example usage
raw_text = "The movie was absolutely fantastic!"
processed = nlp_pipeline(raw_text)
print(processed) # "movie absolutely fantastic"

Text as Data

Learn how to think about text from a data science perspective and its unique properties.