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Natural Language Processing

  • Course level: Beginner

Description

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.

What Will I 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

Topics for this course

46 Lessons

Natural Language Processing

Introduction12:52
Regular Expressions00:00:00
Regular Expressions in Practical NLP00:00:00
Word Tokenization00:00:00
Word Normalization and Stemming00:00:00
Sentence Segmentation00:00:00
Defining Minimum Edit Distance00:00:00
Computing Minimum Edit Distance00:00:00
Backtrace for Computing Alignments00:00:00
Weighted Minimum Edit Distance00:00:00
Minimum Edit Distance in Computational Biology00:00:00
Introduction to N-grams00:00:00
Estimating N-gram Probabilities00:00:00
Evaluation and Perplexity00:00:00
Generalization and Zeros00:00:00
Smoothing Add One00:00:00
Interpolation00:00:00
Good Turing Smoothing00:00:00
Kneser Ney Smoothing00:00:00
The Spelling Correction Task00:00:00
The Noisy Channel Model of Spelling00:00:00
Real Word Spelling Correction00:00:00
State of the Art Systems00:00:00
What is Text Classification00:00:00
Naive Bayes00:00:00
Formalizing the Naive Bayes Classifier00:00:00
Naive Bayes Learning00:00:00
Naive Bayes Relationship to Language Modeling00:00:00
Multinomial Naive Bayes A Worked Example00:00:00
Precision, Recall, and the F measure00:00:00
Text Classification Evaluation00:00:00
Practical Issues in Text Classification00:00:00
What is Sentiment Analysis00:00:00
Sentiment Analysis A baseline algorithm00:00:00
Sentiment Lexicons00:00:00
Learning Sentiment Lexicons00:00:00
Other Sentiment Tasks00:00:00
Generative vs Discriminative Models00:00:00
Making features from text for discriminative NLP models00:00:00
Feature Based Linear Classifiers00:00:00
Building a Maxent Model The Nuts and Bolts00:00:00
Generative vs Discriminative models The problem of overcounting evidence00:00:00
Introduction to Information Extraction00:00:00
Evaluation of Named Entity Recognition00:00:00
Sequence Models for Named Entity Recognition00:00:00
Maximum Entropy Sequence Models00:00:00
Natural Language Processing
Free

Enrolment validity: Lifetime

Requirements

  • It'll help you to have prior experience of coding in Python using data types, writing functions, and importing libraries. Some experience with linguistics and probability is useful but not necessary.