Understanding the basics of natural language processing and its core challenges.

• What is Natural Language Processing
• History and evolution of NLP
• Key challenges in NLP
• Applications and use cases
• NLP pipeline overview
• Linguistic fundamentals
• Statistical vs neural approaches
• Industry applications

Clean and prepare text data for analysis using essential preprocessing techniques.

• Text cleaning and normalization
• Tokenization techniques
• Stop words removal
• Stemming and lemmatization
• Regular expressions
• Unicode handling
• Language detection
• Text quality assessment

Convert text into numerical formats that machine learning algorithms can process.

• Bag of Words (BoW)
• TF-IDF vectorization
• N-gram models
• Word embeddings introduction
• Word2Vec and GloVe
• FastText embeddings
• Document embeddings
• Sparse vs dense representations

Build models that understand and generate human language patterns.

• N-gram language models
• Statistical language modeling
• Neural language models
• Perplexity and evaluation
• Smoothing techniques
• Back-off models
• Modern language models
• Language model applications

Classify and categorize text documents using machine learning techniques.

• Document classification
• Sentiment analysis
• Topic modeling with LDA
• Feature engineering
• Classification algorithms
• Multi-label classification
• Evaluation metrics
• Imbalanced dataset handling

Extract and classify named entities from unstructured text data.

• Entity types and definitions
• Rule-based NER approaches
• Statistical NER models
• Neural NER architectures
• IOB tagging scheme
• Entity linking
• Custom entity recognition
• Evaluation of NER systems

Assign grammatical categories to words in sentences for syntactic analysis.

• POS tag definitions
• Rule-based tagging
• HMM-based tagging
• CRF models for POS
• Neural POS tagging
• Tagset comparison
• Unknown word handling
• Cross-lingual POS tagging

Analyze sentence structure and grammatical relationships between words.

• Constituency parsing
• Dependency parsing
• Context-free grammars
• Chart parsing algorithms
• Statistical parsing
• Neural parsing models
• Parse tree evaluation
• Parsing applications

Build models for tasks like machine translation and text summarization.

• Encoder-decoder architecture
• RNN-based seq2seq
• Attention mechanisms
• LSTM and GRU variants
• Beam search decoding
• Teacher forcing
• Evaluation metrics
• Seq2seq applications

Master the transformer architecture that revolutionized modern NLP.

• Self-attention mechanism
• Multi-head attention
• Transformer architecture
• Positional encodings
• BERT and variants
• GPT models
• Fine-tuning strategies
• Transformer applications

Explore sophisticated NLP applications and real-world implementations.

• Question answering systems
• Chatbots and dialogue
• Text summarization
• Machine translation
• Information extraction
• Text generation
• Multimodal NLP
• Conversational AI

Learn essential NLP libraries and deploy models in production environments.

• NLTK and spaCy libraries
• Transformers library
• Gensim for topic modeling
• Model evaluation metrics
• A/B testing for NLP
• API development
• Cloud NLP services
• Ethical considerations