Gain insights into data science fundamentals, explore machine learning and big data, and delve into real-time projects. Discover systematic approaches to data acquisition, wrangling, and solving diverse problems.

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There is a lot of dispersed, and somewhat conflicting information on the internet when it comes to data science, making it tough to know where to start. Don't worry. 

This course will get you familiar with the state of data science and the related fields such as machine learning and big data. You will be going through the fundamental concepts and libraries which are essential to solve any problem in this field. 

You will work on real-time projects from Kaggle while also honing your mathematical skills which will be used extensively in most problems you face. You will also be taken through a systematic approach to learning about data acquisition to data wrangling and everything in between. This is your all-in-one guide to becoming a confident data scientist.

An Introductory Guide to Data Science and Machine Learning

# 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.

What is Data Science ?

Applications of Data Science

Overview of Libraries

Probability and Statistics

Machine Learning Part-1

Machine Learning Part-2

Machine Learning Part-3

Deep Learning

Machine Learning Tools and Libraries

Big Data Tools and Technologies

Where to go next ?

Spacy Part 2

Named Entity Recognition