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PRODID:IEEE vTools.Events//EN
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TZID:Australia/Melbourne
BEGIN:DAYLIGHT
DTSTART:20201004T030000
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DTSTART:20210404T020000
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BEGIN:VEVENT
DTSTAMP:20210322T074223Z
UID:3EB35F81-BDF1-4EA5-BAA4-92AEB75D0A93
DTSTART;TZID=Australia/Melbourne:20210313T163000
DTEND;TZID=Australia/Melbourne:20210318T163000
DESCRIPTION:Objectives of the Programme:\n1. To give a basic understanding 
 of Machine learning methods and programming.D\n2. To show the applications
  of Machine Learning in different fields.\n3. To give hands-on training in
  Python programming.\n4. To show career prospects and opportunities in Mac
 hine Learning and Data Science.\n\nCo-sponsored by: Department of Geology 
 and IQAC\, Gargaon College\, Dibrugarh University\n\nAgenda: \nPart A: Dat
 a Analytics\n\n1. Introduction to Data Analytics &amp; Machine Learning\n2. Fu
 ndamentals of Data Handling\n3. Data Pre-processing and Visualization\n4. 
 Data Processing and Exploration\nPart B: Machine Learning\n\n1. Concept Le
 arning &amp; the General-to-Specific Ordering\n2. Unsupervised and supervised 
 learning\n3. Decision tree and Bayesian learning\n4. Artificial neural net
 works (ANN)\n\nGargaon\, Assam\, India\, Virtual: https://events.vtools.ie
 ee.org/m/262175
LOCATION:Gargaon\, Assam\, India\, Virtual: https://events.vtools.ieee.org/
 m/262175
ORGANIZER:karishma.rgukt@gmail.com
SEQUENCE:6
SUMMARY:International Workshop on Machine Learning and Data Analytics
URL;VALUE=URI:https://events.vtools.ieee.org/m/262175
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;&lt;strong&gt;Objectives of the Programme: &lt;br /
 &gt;&lt;/strong&gt;1. To give a basic understanding of Machine learning methods and
  programming.D&lt;br /&gt;2. To show the applications of Machine Learning in dif
 ferent fields.&lt;br /&gt;3. To give hands-on training in Python programming.&lt;br
  /&gt;4. To show career prospects and opportunities in Machine Learning and D
 ata Science.&lt;/p&gt;&lt;br /&gt;&lt;br /&gt;Agenda: &lt;br /&gt;&lt;div class=&quot;CjVfdc&quot;&gt;&lt;strong&gt;Part
  A: Data Analytics&amp;nbsp\;&lt;/strong&gt;&lt;/div&gt;\n&lt;p class=&quot;CDt4Ke zfr3Q&quot; dir=&quot;ltr
 &quot;&gt;1. Introduction to Data Analytics &amp;amp\; Machine Learning&lt;br /&gt;2. Fundam
 entals of Data Handling&lt;br /&gt;3. Data Pre-processing and Visualization&lt;br /
 &gt;4. Data Processing and Exploration&lt;/p&gt;\n&lt;div class=&quot;CjVfdc&quot;&gt;&lt;strong&gt;Part 
 B: Machine Learning&amp;nbsp\;&amp;nbsp\;&lt;/strong&gt;&amp;nbsp\;&amp;nbsp\;&lt;/div&gt;\n&lt;p class=&quot;
 CDt4Ke zfr3Q&quot; dir=&quot;ltr&quot;&gt;1. Concept Learning &amp;amp\; the General-to-Specific
  Ordering&lt;br /&gt;2. Unsupervised and supervised learning&lt;br /&gt;3. Decision tr
 ee and Bayesian&amp;nbsp\;learning&amp;nbsp\;&lt;br /&gt;4. Artificial neural networks (
 ANN)&lt;/p&gt;
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