Introduction to Natural Language Processing

Hey there! Are you looking for learning Natural Language Processing? Then this notebook is for you! This notebook contains all basic topics for NLP along multiples examples. It's base on the Coursera Specialization by DeepLearning.AI

Topics:

• Week #1:

  • Vocabulary and Features Extraction
  • Positive and Negative Frequencies
  • Preprocessing Text
  • Logistic Regression (Sigmoid Function & Cost Function)
  • Assigment

• Week #2:

  • Probability and Bayes' Rule
  • Naive Bayes
  • Laplacian Smoothing
  • Log Likelihood
  • Training Naive Bayes
  • Visualizing Navie Bayes
  • Testing Naive Bayes
  • Applications
  • Assumptions
  • Error Analysis
  • Assigment

• Week #3:

  • Vector Space Models
  • Word by Word and Word by Doc
  • Linear Algebra with Numpy!
  • Euclidian Distance
  • Cosine Similarity
  • Manipulating Words in Vector Spaces
  • Principal Component Analysis (PCA)
  • Assigment

• Week #4:

  • Transfroming word vectors
  • K-nearest neighbors
  • Hash tables and Hash functions
  • Locality Sensitive Hashing (LSH)
  • Approximate Nearest Neighbors
  • Searching documents

• Extra Resources:

  • Linear Algebra with Numpy!
  • Words Embeddings

All questions related with the notebook can be ask in Threads to the profile @todo.es.relativo13

Important note: I'm not trying to get any economical retribution, all this content is public and my only intention is share knowledge :)

This notebooks is not finished yet, so it's constanly updated:)