Machine Learning: Natural Language Processing in Python (V2)

Machine Learning: Natural Language Processing in Python (V2)

NLP: From Markov Models to Artificial Intelligence, Deep Learning, Machine Learning, and Data Science in Python

Welcome to Machine Learning: Natural Language Processing in Python (Version 2). This is a massive 4-in-1 course covering: Vector models and text preprocessing methods, Probability models and Markov models, Machine learning methods, Deep learning and neural network methods.

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What you’ll learn

  • How to convert text into vectors using CountVectorizer, TF-IDF, word2vec, and GloVe
  • How to implement a document retrieval system / search engine / similarity search / vector similarity
  • Probability models, language models and Markov models (prerequisite for Transformers, BERT, and GPT-3)
  • How to implement a cipher decryption algorithm using genetic algorithms and language modeling
  • How to implement spam detection
  • How to implement sentiment analysis
  • How to implement an article spinner
  • How to implement text summarization
  • How to implement latent semantic indexing
  • How to implement topic modeling
  • Machine learning (Naive Bayes, Logistic Regression, PCA, SVD, Latent Dirichlet Allocation)
  • Deep learning (ANNs, CNNs, RNNs, LSTM, GRU) (more important prerequisites for BERT and GPT-3)
  • Hugging Face Transformers (VIP only)
  • How to use Python, Scikit-Learn, Tensorflow, +More for NLP
  • Text preprocessing, tokenization, stopwords, lemmatization, and stemming
  • Parts-of-speech tagging and named entity recognition

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