Learn the basics of data science and how to work with important libraries such as NumPy, pandas, Beautiful Soup, etc.

Become a Data Scientist

This module will get us familiar with the state of data science and its fundamental concepts. We'll be going through the applications of data science and a few libraries which are essential to solve any problem in this field, such as Beautiful Soup, Scrapy, NumPy, pandas, spaCy, etc.
We'll also learn about data manipulation with NumPy as well as probability and statistics.
By the end of this module, we'll have basic and essential knowledge of data science and its libraries.

Getting Started with Data Science

# Named Entity Recognition

If we check Wikipedia we get to know the following definition of Named Entity Recognition (NER).
<hr>

**Named Entity Recognition (NER), also known as entity identification, entity chunking, and entity extraction is a subtask of information extraction that seeks to locate and classify a named entity mentioned in unstructured text into pre-defined categories. These categories could include things like person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.**

<hr>

Spacy provides the following named entities.

 <table>
  <tr>
    <th>Type</th>
    <th>Description</th>
  </tr>
  <tr>
    <td>PERSON</td>
    <td>People, including fictional.</td>
  </tr>


  <tr>
    <td>NORP</td>
    <td>Nationalities or religious and political groups</td>
  </tr>

  <tr>
    <td>FAC</td>
    <td>Buildings, airports, highways, bridges, etc.</td>
  </tr>

  <tr>
    <td>ORG</td>
    <td>Companies, agencies, institutions, etc.</td>
  </tr>

  <tr>
    <td>GPE</td>
    <td>Countries, cities, states.</td>
  </tr>


  <tr>
    <td>LOC</td>
    <td>Non-GPE locations, mountain ranges, and bodies of water</td>
  </tr>

  <tr>
    <td>PRODUCT</td>
    <td>Objects, vehicles, foods, etc. (Not services)</td>
  </tr>

  <tr>
    <td>EVENT</td>
    <td>Named hurricanes, battles, wars, sports events, etc.</td>
  </tr>

  <tr>
    <td>WORK_OF_ART</td>
    <td>Titles of books, songs, etc.</td>
  </tr>

<tr>
    <td>LAW</td>
    <td>Named documents made into laws.</td>
  </tr>

<tr>
    <td>LANGUAGE</td>
    <td>Any named language.</td>
  </tr>

<tr>
    <td>DATE</td>
    <td>Absolute or relative dates or periods.</td>
  </tr>


<tr>
    <td>TIME</td>
    <td>Times smaller than a day.</td>
  </tr>

<tr>
    <td>PERCENT</td>
    <td>Percentage, including ”%“.</td>
  </tr>

<tr>
    <td>MONEY</td>
    <td>Monetary values, including unit.</td>
  </tr>


<tr>
    <td>QUANTITY</td>
    <td>Measurements, as of weight or distance.</td>
  </tr>

<tr>
    <td>ORDINAL</td>
    <td>“first”, “second”, etc.</td>
  </tr>


<tr>
    <td>CARDINAL</td>
    <td>Numerals that do not fall under another type.</td>
  </tr>



</table> 



## Coding exercise

Learn more details about Spacy in this lesson.

Spacy Part 2

Named Entity Recognition