Natural Language Processing

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About Course

If Natural Language Processinghasn’t been your forte, Natural Language Processing Fundamentals will make sure you set off to a steady start. This comprehensive guide will show you how to effectively use Python libraries and NLP concepts to solve various problems.

You’ll be introduced to natural language processing and its applications through examples and exercises. This will be followed by an introduction to the initial stages of solving a problem, which includes problem definition, getting text data, and preparing it for modeling. With exposure to concepts like advanced natural language processing algorithms and visualization techniques, you’ll learn how to create applications that can extract information from unstructured data and present it as impactful visuals. Although you will continue to learn NLP-based techniques, the focus will gradually shift to developing useful applications. In these sections, you’ll understand how to apply NLP techniques to answer questions as can be used in chatbots.

By the end of this course, you’ll be able to accomplish a varied range of assignments ranging from identifying the most suitable type of NLP task for solving a problem to using a tool like spacy or genesis for performing sentiment analysis. The course will easily equip you with the knowledge you need to build applications that interpret human language.

 

Who this course is for:

  • Natural Language Processing Fundamentals is designed for novice and mid-level data scientists and machine learning developers who want to gather and analyze text data to build an NLP-powered product.
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What Will You Learn?

  • Obtain, verify, and clean data before transforming it into a correct format for use
  • Perform data analysis and machine learning tasks using Python
  • Understand the basics of computational linguistics
  • Build models for general natural language processing tasks
  • Evaluate the performance of a model with the right metrics
  • Visualize, quantify, and perform exploratory analysis from any text data

Course Content

Natural Language Processing

  • Introduction
    12:52
  • Regular Expressions
    00:00
  • Regular Expressions in Practical NLP
    00:00
  • Word Tokenization
    00:00
  • Word Normalization and Stemming
    00:00
  • Sentence Segmentation
    00:00
  • Defining Minimum Edit Distance
    00:00
  • Computing Minimum Edit Distance
    00:00
  • Backtrace for Computing Alignments
    00:00
  • Weighted Minimum Edit Distance
    00:00
  • Minimum Edit Distance in Computational Biology
    00:00
  • Introduction to N-grams
    00:00
  • Estimating N-gram Probabilities
    00:00
  • Evaluation and Perplexity
    00:00
  • Generalization and Zeros
    00:00
  • Smoothing Add One
    00:00
  • Interpolation
    00:00
  • Good Turing Smoothing
    00:00
  • Kneser Ney Smoothing
    00:00
  • The Spelling Correction Task
    00:00
  • The Noisy Channel Model of Spelling
    00:00
  • Real Word Spelling Correction
    00:00
  • State of the Art Systems
    00:00
  • What is Text Classification
    00:00
  • Naive Bayes
    00:00
  • Formalizing the Naive Bayes Classifier
    00:00
  • Naive Bayes Learning
    00:00
  • Naive Bayes Relationship to Language Modeling
    00:00
  • Multinomial Naive Bayes A Worked Example
    00:00
  • Precision, Recall, and the F measure
    00:00
  • Text Classification Evaluation
    00:00
  • Practical Issues in Text Classification
    00:00
  • What is Sentiment Analysis
    00:00
  • Sentiment Analysis A baseline algorithm
    00:00
  • Sentiment Lexicons
    00:00
  • Learning Sentiment Lexicons
    00:00
  • Other Sentiment Tasks
    00:00
  • Generative vs Discriminative Models
    00:00
  • Making features from text for discriminative NLP models
    00:00
  • Feature Based Linear Classifiers
    00:00
  • Building a Maxent Model The Nuts and Bolts
    00:00
  • Generative vs Discriminative models The problem of overcounting evidence
    00:00
  • Introduction to Information Extraction
    00:00
  • Evaluation of Named Entity Recognition
    00:00
  • Sequence Models for Named Entity Recognition
    00:00
  • Maximum Entropy Sequence Models
    00:00

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