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Learn Natural Language Processing: The Complete Natural Language Processing Course

Learn A-Z everything about Natural Language Processing
From the basic to advanced

What you'll learn

  • Basic fundamentals of Natural Language Processing.
  • NLP components – Natural Language Understanding and Natural Language Generation.
  • NLP Phases - Lexical / Morphological analysis, Syntactic analysis, Semantic analysis, Disclosure integration, Pragmatic analysis, Word Sense Disambiguation.
  • Various Text preprocessing and Feature extraction techniques.
  • Recurrent Neural Networks (RNN), LSTM, GRU, Encoder and Decoder, Transformers and Hugging Face Transformers .
  • Text classification, Text summarization, Text paraphrasing, Grammar correction, Language modeling, Topic modeling, Text generation, Question and Answer generation, Chatbots, Text translation.
  • Project management, development and deployment.
  • Web scraping techniques.
  • API development using FASTAPI framework.
  • Hands on experience in real world projects.
  • Natural Language Processing interview questions.
  • Natural Language Processing mock interview preparation.
  • Helping resume creation.

Requirements

  • Carry your own laptop with decent configurations
  • Knowledge about Python programming language.
  • Machine learning and Deep learning concepts.
  • Familiar with TensorFlow and Pytorch frameworks.

  • Course overview
  • Course outcome
  • Installing anaconda, jupyter notebook
  • Working with environments

  • Introduction of Natural Language Processing
  • Components of NLP – NLU and NLG
  • Importance of NLP
  • Why NLP difficult
  • Industrial and real-world applications of NLP
  • NLP pipeline

  • Lexical / Morphological analysis
  • Syntactic analysis
  • Semantic analysis
  • Disclosure integration
  • Pragmatic analysis
  • Word Sense Disambiguation

  • Getting started with text data
  • Basic operations on text data
  • Splitting and joining strings
  • Working with regular expression on text (Re library)
  • Remove punctuations, digits and stop words
  • Remove emojis and frequent words
  • Remove URLs, Unicode, ASCII codes and HTML tags
  • Spelling correction
  • Stemming and Lemmatization
  • Tokenization
  • Part of Speech Tagging (POS)
  • Name Entity Recognition (NER)
  • Chunking
  • Working with NLTK library
  • Working with SpaCy library
  • Working with TextBlob library
  • Working with Gensim library
  • Project 1
  • Project 2

  • Bag of Word technique
  • TF-IDF technique
  • Word embedding – Word2Vec
  • Text similarities – euclidean distance, cosine similarity and jaccard similarity
  • Working with Word2Vec and Glove libraries
  • Project 1
  • Project 2

  • Introduction of RNN
  • RNN vs ANN
  • Importance of RNN
  • Architecture of RNN
  • Working Process of RNN
  • Building custom RNN model
  • Model fine tuning
  • Limitation of RNN
  • Project 1

  • Introduction of LSTM
  • How LSTM overcome RNN limitation
  • Architecture of LSTM
  • Working process of LSTM
  • Building custom LSTM model
  • Model fine tuning
  • Limitation of LSTM
  • Project 1

  • Introduction of GRU
  • Architecture of GRU
  • Working process of GRU
  • Building custom GRU
  • Model fine tuning
  • Limitation of GRU
  • Project 1

  • Introduction of sequence-to-sequence model
  • Understand the concept of Encoder and Decoder
  • Importance of Encoder and Decoder
  • Architecture of Encoder and Decoder
  • Use cases of Encoder and Decoder
  • Building custom Encoder and Decoder model
  • Model fine tuning
  • Limitation of Encoder and Decoder
  • Project 1

  • Introduction of Attention models
  • Types of Attention models
  • How Attention models enhance the accuracy of Encoder and Decoder
  • Architecture of Attention models
  • Working process of Attention models
  • Building custom Attention models
  • Model fine tuning
  • Limitation of Attention models
  • Project 1

  • Introduction of Transformer
  • Architecture of Transformer
  • Working process of Transformer
  • Understand BERT Transformer and its architecture
  • Building custom Transformer model
  • Model fine tuning
  • Project 1
  • Project 2

  • About Hugging Face
  • Introduction of Hugging Face Transformers
  • Working with Pretrained Transformers by Hugging Face
  • Model Fine tuning
  • Roberta Transformer
  • Distil Bart Transformer
  • T5 Transformer
  • Pegasus Transformer
  • GPT-J & GPT-2 Transformers
  • Project 1
  • Project 2
  • Project 3

  • Introduction of Open AI model
  • Web scraping techniques
  • FASTAPI development
  • GitHub management
  • Project deployment
  • Level up your Kaggle profile

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