BEGIN:VCALENDAR
VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:Asia/Kolkata
BEGIN:STANDARD
DTSTART:19451014T230000
TZOFFSETFROM:+0630
TZOFFSETTO:+0530
TZNAME:IST
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BEGIN:VEVENT
DTSTAMP:20241006T184824Z
UID:AADED585-2663-4143-B1F5-CC2E4856F6A0
DTSTART;TZID=Asia/Kolkata:20241001T140000
DTEND;TZID=Asia/Kolkata:20241001T160000
DESCRIPTION:[]The expert session on &quot;Data Preparation for ML/DL Models&quot; aim
 ed to provide participants with essential skills for effective data handli
 ng in machine learning and deep learning projects. It began by highlightin
 g the importance of high-quality data and the challenges posed by differen
 t data types—structured\, unstructured\, and semi-structured.\n\nKey top
 ics included data cleaning techniques for addressing missing values\, dupl
 icates\, and outliers\, ensuring data integrity for reliable model predict
 ions. The session also covered data transformation methods such as normali
 zation\, standardization\, and encoding categorical variables\, along with
  the significance of feature engineering to enhance model performance.\n\n
 By the end of the session\, attendees gained a clear understanding of the 
 critical role of data preparation and practical strategies to improve thei
 r ML/DL projects.\n\nSpeaker(s): Dr. Shilpa Bhalerao\, \n\nAgenda: \nThe p
 rimary objective of this expert session was to provide attendees with a co
 mprehensive understanding of the critical role data preparation plays in t
 he development of Machine Learning (ML) and Deep Learning (DL) models. Par
 ticipants aimed to learn best practices\, methodologies\, and tools that e
 nhance data quality and\, consequently\, model performance.\n\nRoom: S-13\
 , Bldg: F-Block\, Sage University Indore\, Rau Road\, Dewas-Indore Bypass 
 Road\, INDORE\, Madhya Pradesh\, India\, 452001
LOCATION:Room: S-13\, Bldg: F-Block\, Sage University Indore\, Rau Road\, D
 ewas-Indore Bypass Road\, INDORE\, Madhya Pradesh\, India\, 452001
ORGANIZER:22adv3ari0010@sageuniversity.in
SEQUENCE:3
SUMMARY:Data Preparation for ML/DL Model
URL;VALUE=URI:https://events.vtools.ieee.org/m/437697
X-ALT-DESC:Description: &lt;br /&gt;&lt;p class=&quot;15&quot; align=&quot;justify&quot;&gt;&lt;img src=&quot;https
 ://events.vtools.ieee.org/vtools_ui/media/display/1c927f10-c14f-4e56-a104-
 288caadb03bd&quot; alt=&quot;&quot; height=&quot;800&quot;&gt;The expert session on &quot;Data Preparation 
 for ML/DL Models&quot; aimed to provide participants with essential skills for 
 effective data handling in machine learning and deep learning projects. It
  began by highlighting the importance of high-quality data and the challen
 ges posed by different data types&amp;mdash\;structured\, unstructured\, and s
 emi-structured.&lt;/p&gt;\n&lt;p class=&quot;15&quot; align=&quot;justify&quot;&gt;Key topics included dat
 a cleaning techniques for addressing missing values\, duplicates\, and out
 liers\, ensuring data integrity for reliable model predictions. The sessio
 n also covered data transformation methods such as normalization\, standar
 dization\, and encoding categorical variables\, along with the significanc
 e of feature engineering to enhance model performance.&lt;/p&gt;\n&lt;p class=&quot;15&quot; 
 align=&quot;justify&quot;&gt;By the end of the session\, attendees gained a clear under
 standing of the critical role of data preparation and practical strategies
  to improve their ML/DL projects.&lt;/p&gt;&lt;br /&gt;&lt;br /&gt;Agenda: &lt;br /&gt;&lt;p class=&quot;1
 5&quot; align=&quot;justify&quot;&gt;The primary objective of this expert session was to pro
 vide attendees with a comprehensive understanding of the critical role dat
 a preparation plays in the development of Machine Learning (ML) and Deep L
 earning (DL) models. Participants aimed to learn best practices\, methodol
 ogies\, and tools that enhance data quality and\, consequently\, model per
 formance.&lt;/p&gt;
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