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TZID:Asia/Riyadh
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DTSTART:20380119T061407
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DTSTART:19470313T235308
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BEGIN:VEVENT
DTSTAMP:20250330T101747Z
UID:C498B594-FE0E-4D7F-B30D-4C49FF1FCB6F
DTSTART;TZID=Asia/Riyadh:20250427T200000
DTEND;TZID=Asia/Riyadh:20250427T213000
DESCRIPTION:Workshop Series Introduction\n\nThis workshop is one of a serie
 s of workshops that aim to enhance understanding of data modeling processe
 s for those interested in artificial intelligence and machine learning. Sp
 ecifically\, it is designed to prepare participants for the first edition 
 of the upcoming [IEEEModelthon 1.0](https://events.vtools.ieee.org/m/44393
 8) on [Kaggle](https://www.kaggle.com/).\n\nWorkshop Overview\n\nThe next 
 phase after applying data preprocessing is to run the machine learning alg
 orithm. The result of this phase directly affects the performance of the M
 L algorithm. So\, it is critical to have the data ready in its final form 
 with no wrong data\, no missing values\, no outliers\, no duplicated rows\
 , etc.\n\nPurpose\n\nThe primary purpose of this phase is to have the data
  in its final form\, ready to use for the ML algorithm. Some ML algorithms
  work well with categorical data and apply embedded encoding techniques an
 d others do not (we should manually encode categorical columns)\, some of 
 them deal well with missing values and apply an embedded imputation and so
 me do not\, and very many considerations!\n\nObjective and Outcome\n\nThis
  workshop is essential for anyone in a data-driven field. It introduces me
 thods for data preprocessing and cleaning. The main outcome of this phase 
 is to understand these methods in order to create a data preprocessing pip
 eline.\n\nFor more information about the IEEEModelthon1.0\, click [here](h
 ttps://events.vtools.ieee.org/m/443938).\n\nVirtual: https://events.vtools
 .ieee.org/m/444085
LOCATION:Virtual: https://events.vtools.ieee.org/m/444085
ORGANIZER:ieee.computersociety.aabu@gmail.com
SEQUENCE:21
SUMMARY:Scikit-Learn Essentials: Data Preprocessing Techniques
URL;VALUE=URI:https://events.vtools.ieee.org/m/444085
X-ALT-DESC:Description: &lt;br /&gt;&lt;h3&gt;&lt;strong&gt;Workshop Series Introduction&lt;/str
 ong&gt;&lt;/h3&gt;\n&lt;p&gt;This workshop is one of a series of workshops that aim to en
 hance understanding of data modeling processes for those interested in art
 ificial intelligence and machine learning. Specifically\, it is designed t
 o prepare participants for the first edition of the upcoming&amp;nbsp\;&lt;a titl
 e=&quot;IEEEModelthon 1.0&quot; href=&quot;https://events.vtools.ieee.org/m/443938&quot; targe
 t=&quot;_blank&quot; rel=&quot;noopener&quot;&gt;&lt;em&gt;&lt;strong&gt;IEEEModelthon 1.0&lt;/strong&gt;&lt;/em&gt;&lt;/a&gt;&amp;
 nbsp\;on&amp;nbsp\;&lt;em&gt;&lt;a title=&quot;Kaggle&quot; href=&quot;https://www.kaggle.com/&quot; target
 =&quot;_blank&quot; rel=&quot;noopener&quot;&gt;&lt;strong&gt;Kaggle&lt;/strong&gt;&lt;/a&gt;&lt;/em&gt;.&lt;/p&gt;\n&lt;h3&gt;&lt;br&gt;&lt;s
 trong&gt;Workshop Overview&lt;/strong&gt;&lt;/h3&gt;\n&lt;p&gt;The next phase after applying &lt;s
 trong&gt;data preprocessing&lt;/strong&gt; is to run the machine learning algorithm
 . The result of this phase directly affects the performance of the ML algo
 rithm. So\, it is critical to have the data ready in its final form with n
 o wrong data\, no missing values\, no outliers\, no duplicated rows\, etc.
 &lt;br&gt;&lt;br&gt;&lt;/p&gt;\n&lt;h3&gt;&lt;strong&gt;Purpose&lt;/strong&gt;&lt;/h3&gt;\n&lt;p dir=&quot;ltr&quot;&gt;The primary 
 purpose of this phase is to have the data in its final form\, ready to use
  for the ML algorithm. Some ML algorithms work well with categorical data 
 and apply embedded &lt;em&gt;encoding&lt;/em&gt; techniques and others do not (we shou
 ld manually encode categorical columns)\, some of them deal well with &lt;em&gt;
 missing&lt;/em&gt; values and apply an embedded &lt;em&gt;imputation&lt;/em&gt; and some do 
 not\, and very many considerations!&lt;br&gt;&lt;br&gt;&lt;/p&gt;\n&lt;h3&gt;&lt;strong&gt;Objective and
  Outcome&lt;/strong&gt;&lt;/h3&gt;\n&lt;div&gt;This workshop is essential for anyone in a da
 ta-driven field.&amp;nbsp\;It introduces methods for &lt;strong&gt;data&lt;/strong&gt; &lt;st
 rong&gt;preprocessing&lt;/strong&gt; and &lt;strong&gt;cleaning&lt;/strong&gt;. The main outcom
 e of this phase is to understand these methods in order to create a &lt;stron
 g&gt;data preprocessing pipeline&lt;/strong&gt;.&lt;br&gt;&lt;br&gt;&lt;br&gt;For more information ab
 out the&amp;nbsp\;&lt;strong&gt;IEEEModelthon1.0&lt;/strong&gt;\, click&amp;nbsp\;&lt;a title=&quot;IE
 EEModelthon1.0&quot; href=&quot;https://events.vtools.ieee.org/m/443938&quot; target=&quot;_bl
 ank&quot; rel=&quot;noopener&quot;&gt;here&lt;/a&gt;.&lt;/div&gt;
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