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Home » Technology » Big Data & Data Science » Natural Language Processing for Hackers

Course Skill Level:

Foundational to Intermediate

Course Duration:

6 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:

    NLPRHAL21E09

Who should attend & recommended skills:

Those experienced with Python with basic IT & Linux skills

Who should attend & recommended skills

  • This course is geared for Python experienced developers, analysts or others with Python skills who want to know the techniques to analyze, interpret, and create human-understandable text and speech. Advances in machine learning have pushed NLP to new levels of accuracy and uncanny realism
  • This course is geared for attendees with Python skills who wish to know the techniques to analyze, interpret, and create human-understandable text and speech.
  • Skill-level: Foundation-level Natural-Language-Processing skills for Intermediate skilled team members. This is not a basic class.
  • IT skills: Basic to intermediate (1-4 years’ experience)
  • Python: Basic (1-2 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su
  • Natural Language Processing: Not required
  • Machine Learning: Not required

About this course

Natural Language Processing for Hackers covers NLP end-to-end, giving you the skills and techniques that allow your computers to speak human. Unlike many research-oriented courses that use the kind of clean datasets you would never find in the real world; this practical guide takes on NLP as you’ll actually use it. You’ll learn the key concepts of NLP by coding your own tools and projects, from a text analysis service right up to a full-featured chatbot. Everything is written in concise, easy-to-read Python code to ensure you’ll grok the most important aspects of Natural Language Processing. When you’re done, you will be able to apply the complete range of NLP techniques to build practical applications – even with messy real-world data.

Skills acquired & topics covered

  • Key concepts of NLP by coding your own tools and projects, from a text analysis service right up to a full-featured chatbot.
  • Everything is written in concise, easy-to-read Python code to ensure:
  • You’ll grok the most important aspects of Natural Language Processing.
  • You will be able to apply the complete range of NLP techniques to build practical applications – even with messy real-world data.
  • Constructing your own Text Analysis engine
  • Building a Twitter listener that performs Sentiment Analysis on a certain subject
  • Assembling your own NLP toolbox, complete with Part of Speech Tagger, Shallow Parser, Named Entity Extractor, and Dependency Parsers
  • Cleaning and standardizing messy datasets

Course breakdown / modules

  • Installing NLTK
  • Splitting Text
  • Building a vocabulary
  • Fun with Bigrams and Trigrams
  • Part of Speech Tagging
  • Named Entity Recognition

  • Wordnet Structure
  • Lemma Operations

  • How stemmers work
  • How lemmatizes work

  • A Practical Machine Learning Example

  • Installing Scikit-Learn and building a dataset
  • Training a Scikit-Learn Model
  • Making Predictions

  • Existing corpora
  • Ideas for Gathering Data
  • Getting the Data

  • Text Feature Extractor
  • Scikit-Learn Feature Extraction
  • Text Classification with Naive Bayes

  • Building a Flask API Deploy to Heroku

  • Be Aware of Negations
  • Machine Learning doesn’t get Humor
  • Multiple and Mixed Sentiments
  • Non-Verbal Communication

  • Twitter Corpora
  • Other Sentiment Analysis Corpora
  • Building a Tweets Dataset
  • Sentiment Analysis – A First Attempt
  • Better Tokenization

  • Try a different classifier
  • Use Ngrams Instead of Words
  • Using a Pipeline
  • Cross Validation
  • Grid Search
  • Picking the Best Tokenizer

  • Classification Metrics
  • Binary Classification

  • The Confusion Matrix

  • Part-Of-Speech Corpora
  • Building Toy Models
  • About Feature Extraction
  • Using the NLTK Base Classes
  • Writing the Feature Extractor
  • Training the Tagger
  • Out-Of-Core Learning

  • IOB Tagging
  • Implementing the Chunk Parser
  • Chunker Feature Detection

  • NER Corpora
  • The Groningen Meaning Bank Corpus
  • Feature Detection
  • NER Training

  • Train the Platform via Examples
  • Action Handlers

  • Chatbot Base Class and Training Set
  • Training the Chatbot Everything together

  • The Movie DB API
  • Small-Talk Handlers
  • Simple Handlers
  • Execution Handlers

  • Installing ngrok
  • Setting up Facebook

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