Natural Language Processing Fundamentals

Course Description Overview

Course Number:
035454
Course Length:
3 days
Course Description Overview:

If NLP hasn'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 gensim for performing sentiment analysis. The course will easily equip you with the knowledge you need to build applications that interpret human language.

 

This is a three-day course that starts with basics and goes on to explain various NLP tools and techniques that equip you with all that you need to solve common business problems for processing text.
Course Objectives:

This course takes a hands-on approach to the practical aspects of using Python to build NLP applications. It contains multiple activities that use real-life business scenarios for you to practice and apply your new skills in a highly relevant context. Here is the list of course objectives:

  • Learn about space and time complexities express them using big O notation
  • Explore various classic sorting algorithms, such as merge and quick sort
  • Understand the workings of basic (Lists, queues and stacks) and complex data structures (hash tables and binary trees)
  • Gain an insight into various algorithm design paradigms (Greedy, Divide and Conquer and Dynamic programming)
  • Discover string matching techniques
  • Master graph representations and learn about different graph algorithms, such as cycle detection, traversal and shortest path
Target Student:
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. 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.
Prerequisites:
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Course-specific Technical Requirements Software:

  • Operating system: Windows 7 SP1 32/64-bit, Windows 8.1 32/64-bit, Windows 10 32/64-bit, Ubuntu 14.04 or later, or macOS Sierra or later
  • Browser: Google Chrome or Mozilla Firefox
  • Conda
  • Jupyterlab
  • Python 3.x
    • Course-specific Technical Requirements Hardware:

    This course will require a computer system for the instructor and one for each student. The minimum hardware requirements are as follows:

    • Processor: Dual Core or better
    • Memory: 4 GB RAM
    • Hard disk: 10 GB
    • Internet connection
    Certification reference (where applicable)
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    Course Content:

    Lesson 1: Introduction to NLP

    • What is natural language processing (NLP)?
    • Types of natural language processing tasks
    • Structuring a natural language processing project

     

    Lesson 2: Extraction Methods from Unstructured Text

    • Tokenization methods
    • Term frequency observations
    • Bag-of-Words and TF-IDF


    Lesson 3: Building a Simple Classifier

    • Basic theoretical coverage and sample code of Supervised and Unsupervised
    • Classifiers vs. regressors
    • Sampling and splitting data for training algorithms
    • Evaluating the performance of a model
    • Use of Pandas and scikit-learn


    Lesson 4: Collecting Text Data

    • Retrieve and process web page data using urllib, bs4
    • Handle various types of data such as JSON, XML
    • Retrieve real-time data using API provided by the website


    Lesson 5: Topic Modeling

    • Loading and preprocessing documents into a notecourse
    • Training an LDA model to detect the topics in the document
    • Visually represent the topics found in a set of documents


    Lesson 6: Text Summarization and Text Generation

    • Summarizing document using word frequency
    • Generating random text using the markov chain
    • Compare the results between recent methods


    Lesson 7: Vector Representation

    • Converting words to word vectors
    • Perform math-like operations on word vectors e.g. king - man = queen
    • Converting documents to document vectors.
    • Using document vectors to measure the similarity between documents


    Lesson 8: Sentiment Analysis

    • Load a labelled dataset of movie reviews
    • Use word vectors to represent the words in the movie review
    • Train a simple model to predict whether the movie review is positive or negative
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