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TZID:Europe/Lisbon
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DTSTART:20260329T020000
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DTSTART:20261025T010000
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DTSTAMP:20260606T110444Z
UID:ACBC982F-A278-4B6F-9C86-BC51BD450386
DTSTART;TZID=Europe/Lisbon:20260608T170000
DTEND;TZID=Europe/Lisbon:20260702T193000
DESCRIPTION:Machine Learning Bootcamp\n\nA 6-session bootcamp designed to p
 rovide participants with a solid foundation in Machine Learning\, from cor
 e concepts to practical implementation. Throughout the bootcamp\, members 
 will explore data preprocessing\, exploratory data analysis\, supervised l
 earning algorithms\, model evaluation\, and performance optimization. Thro
 ugh hands-on exercises and real-world datasets\, participants developed th
 e skills needed to build\, train\, and evaluate machine learning models wh
 ile gaining insight into the complete machine learning workflow.\n\nSessio
 n 1 (8 June): Introduction to AI\nSession 2(10 June): Exploratory Data Ana
 lysis\nSession 3(17 June): Introduction to Regression\nSession 4(18 June):
  Classification\nSession 5(1 July): Introduction to Computer Vision\nSessi
 on 6(2 July): NLP &amp; Time Series\n\nSpeaker(s): Imen Masmoudi\, \n\nVirtual
 : https://events.vtools.ieee.org/m/562674
LOCATION:Virtual: https://events.vtools.ieee.org/m/562674
ORGANIZER:hadjaliyosr@ieee.org
SEQUENCE:24
SUMMARY:Machine Learning Bootcamp
URL;VALUE=URI:https://events.vtools.ieee.org/m/562674
X-ALT-DESC:Description: &lt;br /&gt;&lt;h2 style=&quot;text-align: center\;&quot;&gt;&lt;span style=
 &quot;font-family: &#39;comic sans ms&#39;\, sans-serif\; color: rgb(230\, 126\, 35)\;&quot;
 &gt;Machine Learning Bootcamp&lt;/span&gt;&lt;/h2&gt;\n&lt;p&gt;&lt;span style=&quot;font-family: &#39;comi
 c sans ms&#39;\, sans-serif\; color: rgb(0\, 0\, 0)\;&quot;&gt;A 6-session bootcamp de
 signed to provide participants with a solid foundation in Machine Learning
 \, from core concepts to practical implementation. Throughout the bootcamp
 \, members will explore data preprocessing\, exploratory data analysis\, s
 upervised learning algorithms\, model evaluation\, and performance optimiz
 ation. Through hands-on exercises and real-world datasets\, participants d
 eveloped the skills needed to build\, train\, and evaluate machine learnin
 g models while gaining insight into the complete machine learning workflow
 .&lt;/span&gt;&lt;/p&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;div&gt;&lt;strong&gt;Session 1 (8 June):&lt;/strong&gt;&amp;nb
 sp\;Introduction to AI&lt;/div&gt;\n&lt;div&gt;&lt;strong&gt;Session 2(10 June):&lt;/strong&gt;&amp;nb
 sp\;Exploratory Data Analysis&lt;/div&gt;\n&lt;div&gt;&lt;strong&gt;Session 3(17 June):&lt;/str
 ong&gt;&amp;nbsp\;Introduction to Regression&lt;/div&gt;\n&lt;div&gt;&lt;strong&gt;Session 4(18 Jun
 e): &lt;/strong&gt;Classification&lt;/div&gt;\n&lt;div&gt;&lt;strong&gt;Session 5(1 July): &lt;/stron
 g&gt;Introduction to Computer Vision&lt;/div&gt;\n&lt;div&gt;&lt;strong&gt;Session 6(2 July):&lt;/
 strong&gt; NLP &amp;amp\; Time Series&lt;/div&gt;\n&lt;div&gt;&amp;nbsp\;&lt;/div&gt;
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