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DESCRIPTION:Join us in celebrating IEEE Day 2025 with an interactive webina
 r on Python &amp; Machine Learning: From Basics to Applications.\n\nThis 2-hou
 r hands-on session will introduce Python fundamentals\, data analysis with
  NumPy &amp; Pandas\, visualization with Matplotlib\, and the basics of machin
 e learning (regression\, classification\, and model training). Participant
 s will gain practical skills to analyze data\, build simple ML models\, an
 d explore pathways to more advanced AI topics.\n\nOpen to students\, engin
 eers\, researchers\, and early-career professionals\, this session is desi
 gned to make Python and ML accessible\, practical\, and inspiring.\n\nSpea
 ker(s): Mostafa\n\nAgenda: \nWebinar Plan (2 Hours)\n\nTitle: Python &amp; Mac
 hine Learning: From Basics to Applications\nAudience: Engineers\, research
 ers\, students\, early-career professionals in data/AI\nGoal: Give partici
 pants a crash introduction to Python for data analysis\, then show how mac
 hine learning solves real problems.\n\nLearning Outcomes\n\nBy the end of 
 this session\, participants will:\n\n- Be comfortable with Python basics (
 syntax\, libraries\, data handling).\n- Use Pandas &amp; NumPy for quick data 
 analysis.\n- Understand the intuition of regression &amp; classification.\n- T
 rain and evaluate a simple ML model in Python.\n- Know how to explore deep
 er topics like clustering &amp; deep learning.\n\nTimed Agenda (120 minutes)\n
 \n00:00 – 00:05 | Welcome &amp; Setup\n\n- Poll: Python experience (none / b
 eginner / intermediate).\n- Share GitHub/Colab link with starter notebook.
 \n\n00:05 – 00:25 | Python Basics Refresher\n\n- Quick intro: syntax\, v
 ariables\, loops\, functions.\n- Libraries for ML: NumPy\, Pandas\, Matplo
 tlib\, Scikit-learn.\n- Demo: Load a CSV\, calculate summary statistics\, 
 plot a histogram.\n\n00:25 – 00:55 | Data Analysis with Python\n\n- Pand
 as: dataframes\, filtering\, grouping.\n- NumPy: arrays\, operations.\n- M
 atplotlib: visualization basics.\n- Hands-on mini demo:\n\n- Load a datase
 t (e.g.\, student grades or Iris dataset).\n- Clean missing values.\n- Plo
 t correlation heatmap.\n\n00:55 – 01:35 | Machine Learning Fundamentals\
 n\n- Concepts: Supervised vs. unsupervised learning.\n- Regression: Predic
 ting continuous values.\n- Classification: Label prediction.\n- Hands-on D
 emo (live in Colab):\n\nBldg: National Wind Institute\, 1009 Canton Avenue
 \, National Wind Institute\, Lubbock\, Texas\, United States\, 79409\, Vir
 tual: https://events.vtools.ieee.org/m/504614
LOCATION:Bldg: National Wind Institute\, 1009 Canton Avenue\, National Wind
  Institute\, Lubbock\, Texas\, United States\, 79409\, Virtual: https://ev
 ents.vtools.ieee.org/m/504614
ORGANIZER:m.chamana@ttu.edu
SEQUENCE:61
SUMMARY:IEEE Day 2025 Celebration - Workshop
URL;VALUE=URI:https://events.vtools.ieee.org/m/504614
X-ALT-DESC:Description: &lt;br /&gt;&lt;p data-start=&quot;62&quot; data-end=&quot;195&quot;&gt;Join us in 
 celebrating &lt;strong data-start=&quot;85&quot; data-end=&quot;102&quot;&gt;IEEE Day 2025&lt;/strong&gt; 
 with an interactive webinar on &lt;strong data-start=&quot;134&quot; data-end=&quot;192&quot;&gt;Pyt
 hon &amp;amp\; Machine Learning: From Basics to Applications&lt;/strong&gt;.&lt;/p&gt;\n&lt;p
  data-start=&quot;197&quot; data-end=&quot;556&quot;&gt;This &lt;strong data-start=&quot;202&quot; data-end=&quot;2
 29&quot;&gt;2-hour hands-on session&lt;/strong&gt; will introduce Python fundamentals\, 
 data analysis with &lt;strong data-start=&quot;285&quot; data-end=&quot;303&quot;&gt;NumPy &amp;amp\; Pa
 ndas&lt;/strong&gt;\, visualization with &lt;strong data-start=&quot;324&quot; data-end=&quot;338&quot;
 &gt;Matplotlib&lt;/strong&gt;\, and the basics of &lt;strong data-start=&quot;358&quot; data-end
 =&quot;378&quot;&gt;machine learning&lt;/strong&gt; (regression\, classification\, and model 
 training). Participants will gain practical skills to analyze data\, build
  simple ML models\, and explore pathways to more advanced AI topics.&lt;/p&gt;\n
 &lt;p data-start=&quot;558&quot; data-end=&quot;726&quot;&gt;Open to &lt;strong data-start=&quot;566&quot; data-e
 nd=&quot;634&quot;&gt;students\, engineers\, researchers\, and early-career professiona
 ls&lt;/strong&gt;\, this session is designed to make Python and ML &lt;strong data-
 start=&quot;683&quot; data-end=&quot;723&quot;&gt;accessible\, practical\, and inspiring&lt;/strong&gt;
 .&lt;/p&gt;&lt;br /&gt;&lt;br /&gt;Agenda: &lt;br /&gt;&lt;p class=&quot;MsoNormal&quot;&gt;&lt;strong&gt;&lt;span style=&quot;f
 ont-family: &#39;Times New Roman&#39;\,serif\;&quot;&gt;Webinar Plan (2 Hours)&lt;/span&gt;&lt;/str
 ong&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot;&gt;&lt;strong&gt;&lt;span style=&quot;font-family: &#39;Times Ne
 w Roman&#39;\,serif\;&quot;&gt;Title:&lt;/span&gt;&lt;/strong&gt;&lt;span style=&quot;font-family: &#39;Times 
 New Roman&#39;\,serif\;&quot;&gt; Python &amp;amp\; Machine Learning: From Basics to Appli
 cations&lt;br&gt;&lt;strong&gt;Audience:&lt;/strong&gt; Engineers\, researchers\, students\,
  early-career professionals in data/AI&lt;br&gt;&lt;strong&gt;Goal:&lt;/strong&gt; Give part
 icipants a crash introduction to Python for data analysis\, then show how 
 machine learning solves real problems.&lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot;&gt;&lt;s
 trong&gt;&lt;span style=&quot;font-family: &#39;Times New Roman&#39;\,serif\;&quot;&gt;Learning Outco
 mes&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot;&gt;&lt;span style=&quot;font-family: &#39;T
 imes New Roman&#39;\,serif\;&quot;&gt;By the end of this session\, participants will:&lt;
 /span&gt;&lt;/p&gt;\n&lt;ol style=&quot;margin-top: 0in\;&quot; start=&quot;1&quot; type=&quot;1&quot;&gt;\n&lt;li class=&quot;
 MsoNormal&quot; style=&quot;mso-list: l3 level1 lfo1\; tab-stops: list .5in\;&quot;&gt;&lt;span
  style=&quot;font-family: &#39;Times New Roman&#39;\,serif\;&quot;&gt;Be comfortable with Pytho
 n basics (syntax\, libraries\, data handling).&lt;/span&gt;&lt;/li&gt;\n&lt;li class=&quot;Mso
 Normal&quot; style=&quot;mso-list: l3 level1 lfo1\; tab-stops: list .5in\;&quot;&gt;&lt;span st
 yle=&quot;font-family: &#39;Times New Roman&#39;\,serif\;&quot;&gt;Use Pandas &amp;amp\; NumPy for 
 quick data analysis.&lt;/span&gt;&lt;/li&gt;\n&lt;li class=&quot;MsoNormal&quot; style=&quot;mso-list: l
 3 level1 lfo1\; tab-stops: list .5in\;&quot;&gt;&lt;span style=&quot;font-family: &#39;Times N
 ew Roman&#39;\,serif\;&quot;&gt;Understand the intuition of regression &amp;amp\; classifi
 cation.&lt;/span&gt;&lt;/li&gt;\n&lt;li class=&quot;MsoNormal&quot; style=&quot;mso-list: l3 level1 lfo1
 \; tab-stops: list .5in\;&quot;&gt;&lt;span style=&quot;font-family: &#39;Times New Roman&#39;\,se
 rif\;&quot;&gt;Train and evaluate a simple ML model in Python.&lt;/span&gt;&lt;/li&gt;\n&lt;li cl
 ass=&quot;MsoNormal&quot; style=&quot;mso-list: l3 level1 lfo1\; tab-stops: list .5in\;&quot;&gt;
 &lt;span style=&quot;font-family: &#39;Times New Roman&#39;\,serif\;&quot;&gt;Know how to explore 
 deeper topics like clustering &amp;amp\; deep learning.&lt;/span&gt;&lt;/li&gt;\n&lt;/ol&gt;\n&lt;p
  class=&quot;MsoNormal&quot;&gt;&lt;strong&gt;&lt;span style=&quot;font-family: &#39;Times New Roman&#39;\,se
 rif\;&quot;&gt;Timed Agenda (120 minutes)&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal
 &quot;&gt;&lt;strong&gt;&lt;span style=&quot;font-family: &#39;Times New Roman&#39;\,serif\;&quot;&gt;00:00 &amp;nda
 sh\; 00:05 | Welcome &amp;amp\; Setup&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;ul style=&quot;margin-t
 op: 0in\;&quot; type=&quot;disc&quot;&gt;\n&lt;li class=&quot;MsoNormal&quot; style=&quot;mso-list: l4 level1 
 lfo2\; tab-stops: list .5in\;&quot;&gt;&lt;span style=&quot;font-family: &#39;Times New Roman&#39;
 \,serif\;&quot;&gt;Poll: Python experience (none / beginner / intermediate).&lt;/span
 &gt;&lt;/li&gt;\n&lt;li class=&quot;MsoNormal&quot; style=&quot;mso-list: l4 level1 lfo2\; tab-stops:
  list .5in\;&quot;&gt;&lt;span style=&quot;font-family: &#39;Times New Roman&#39;\,serif\;&quot;&gt;Share 
 GitHub/Colab link with starter notebook.&lt;/span&gt;&lt;/li&gt;\n&lt;/ul&gt;\n&lt;p class=&quot;Mso
 Normal&quot;&gt;&lt;strong&gt;&lt;span style=&quot;font-family: &#39;Times New Roman&#39;\,serif\;&quot;&gt;00:0
 5 &amp;ndash\; 00:25 | Python Basics Refresher&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;ul style=
 &quot;margin-top: 0in\;&quot; type=&quot;disc&quot;&gt;\n&lt;li class=&quot;MsoNormal&quot; style=&quot;mso-list: l
 1 level1 lfo3\; tab-stops: list .5in\;&quot;&gt;&lt;span style=&quot;font-family: &#39;Times N
 ew Roman&#39;\,serif\;&quot;&gt;Quick intro: syntax\, variables\, loops\, functions.&lt;/
 span&gt;&lt;/li&gt;\n&lt;li class=&quot;MsoNormal&quot; style=&quot;mso-list: l1 level1 lfo3\; tab-st
 ops: list .5in\;&quot;&gt;&lt;span style=&quot;font-family: &#39;Times New Roman&#39;\,serif\;&quot;&gt;Li
 braries for ML: NumPy\, Pandas\, Matplotlib\, Scikit-learn.&lt;/span&gt;&lt;/li&gt;\n&lt;
 li class=&quot;MsoNormal&quot; style=&quot;mso-list: l1 level1 lfo3\; tab-stops: list .5i
 n\;&quot;&gt;&lt;span style=&quot;font-family: &#39;Times New Roman&#39;\,serif\;&quot;&gt;Demo: Load a CS
 V\, calculate summary statistics\, plot a histogram.&lt;/span&gt;&lt;/li&gt;\n&lt;/ul&gt;\n&lt;
 p class=&quot;MsoNormal&quot;&gt;&lt;strong&gt;&lt;span style=&quot;font-family: &#39;Times New Roman&#39;\,s
 erif\;&quot;&gt;00:25 &amp;ndash\; 00:55 | Data Analysis with Python&lt;/span&gt;&lt;/strong&gt;&lt;/
 p&gt;\n&lt;ul style=&quot;margin-top: 0in\;&quot; type=&quot;disc&quot;&gt;\n&lt;li class=&quot;MsoNormal&quot; styl
 e=&quot;mso-list: l2 level1 lfo4\; tab-stops: list .5in\;&quot;&gt;&lt;span style=&quot;font-fa
 mily: &#39;Times New Roman&#39;\,serif\;&quot;&gt;Pandas: dataframes\, filtering\, groupin
 g.&lt;/span&gt;&lt;/li&gt;\n&lt;li class=&quot;MsoNormal&quot; style=&quot;mso-list: l2 level1 lfo4\; ta
 b-stops: list .5in\;&quot;&gt;&lt;span style=&quot;font-family: &#39;Times New Roman&#39;\,serif\;
 &quot;&gt;NumPy: arrays\, operations.&lt;/span&gt;&lt;/li&gt;\n&lt;li class=&quot;MsoNormal&quot; style=&quot;ms
 o-list: l2 level1 lfo4\; tab-stops: list .5in\;&quot;&gt;&lt;span style=&quot;font-family:
  &#39;Times New Roman&#39;\,serif\;&quot;&gt;Matplotlib: visualization basics.&lt;/span&gt;&lt;/li&gt;
 \n&lt;li class=&quot;MsoNormal&quot; style=&quot;mso-list: l2 level1 lfo4\; tab-stops: list 
 .5in\;&quot;&gt;&lt;span style=&quot;font-family: &#39;Times New Roman&#39;\,serif\;&quot;&gt;Hands-on min
 i demo: &lt;/span&gt;&lt;/li&gt;\n&lt;ul style=&quot;margin-top: 0in\;&quot; type=&quot;circle&quot;&gt;\n&lt;li cl
 ass=&quot;MsoNormal&quot; style=&quot;mso-list: l2 level2 lfo4\; tab-stops: list 1.0in\;&quot;
 &gt;&lt;span style=&quot;font-family: &#39;Times New Roman&#39;\,serif\;&quot;&gt;Load a dataset (e.g
 .\, student grades or Iris dataset).&lt;/span&gt;&lt;/li&gt;\n&lt;li class=&quot;MsoNormal&quot; st
 yle=&quot;mso-list: l2 level2 lfo4\; tab-stops: list 1.0in\;&quot;&gt;&lt;span style=&quot;font
 -family: &#39;Times New Roman&#39;\,serif\;&quot;&gt;Clean missing values.&lt;/span&gt;&lt;/li&gt;\n&lt;l
 i class=&quot;MsoNormal&quot; style=&quot;mso-list: l2 level2 lfo4\; tab-stops: list 1.0i
 n\;&quot;&gt;&lt;span style=&quot;font-family: &#39;Times New Roman&#39;\,serif\;&quot;&gt;Plot correlatio
 n heatmap.&lt;/span&gt;&lt;/li&gt;\n&lt;/ul&gt;\n&lt;/ul&gt;\n&lt;p class=&quot;MsoNormal&quot;&gt;&lt;strong&gt;&lt;span s
 tyle=&quot;font-family: &#39;Times New Roman&#39;\,serif\;&quot;&gt;00:55 &amp;ndash\; 01:35 | Mach
 ine Learning Fundamentals&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;ul style=&quot;margin-top: 0in\
 ;&quot; type=&quot;disc&quot;&gt;\n&lt;li class=&quot;MsoNormal&quot; style=&quot;mso-list: l0 level1 lfo5\; t
 ab-stops: list .5in\;&quot;&gt;&lt;strong&gt;&lt;span style=&quot;font-family: &#39;Times New Roman&#39;
 \,serif\;&quot;&gt;Concepts:&lt;/span&gt;&lt;/strong&gt;&lt;span style=&quot;font-family: &#39;Times New R
 oman&#39;\,serif\;&quot;&gt; Supervised vs. unsupervised learning.&lt;/span&gt;&lt;/li&gt;\n&lt;li cl
 ass=&quot;MsoNormal&quot; style=&quot;mso-list: l0 level1 lfo5\; tab-stops: list .5in\;&quot;&gt;
 &lt;strong&gt;&lt;span style=&quot;font-family: &#39;Times New Roman&#39;\,serif\;&quot;&gt;Regression:&lt;
 /span&gt;&lt;/strong&gt;&lt;span style=&quot;font-family: &#39;Times New Roman&#39;\,serif\;&quot;&gt; Pred
 icting continuous values.&lt;/span&gt;&lt;/li&gt;\n&lt;li class=&quot;MsoNormal&quot; style=&quot;mso-li
 st: l0 level1 lfo5\; tab-stops: list .5in\;&quot;&gt;&lt;strong&gt;&lt;span style=&quot;font-fam
 ily: &#39;Times New Roman&#39;\,serif\;&quot;&gt;Classification:&lt;/span&gt;&lt;/strong&gt;&lt;span styl
 e=&quot;font-family: &#39;Times New Roman&#39;\,serif\;&quot;&gt; Label prediction.&lt;/span&gt;&lt;/li&gt;
 \n&lt;li class=&quot;MsoNormal&quot; style=&quot;mso-list: l0 level1 lfo5\; tab-stops: list 
 .5in\;&quot;&gt;&lt;strong&gt;&lt;span style=&quot;font-family: &#39;Times New Roman&#39;\,serif\;&quot;&gt;Hand
 s-on Demo (live in Colab):&lt;/span&gt;&lt;/strong&gt;&lt;/li&gt;\n&lt;/ul&gt;
END:VEVENT
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