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PRODID:-//KnowAFest//Campus Events//EN
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UID:eventsphere-64020@www.knowafest.com
DTSTAMP:20261008T141910Z
DTSTART:20250912T183000Z
DTEND:20250913T182959Z
SUMMARY:Python for Machine Learning Workshop 2025
LOCATION:Top Engineers\, Chennai
DESCRIPTION:<p>Python for Machine Learning - Agenda<br />\n1. Introduction 
 &amp\; Motivation<br />\nWhy Python is the most popular language for ML.<b
 r />\nOverview of today’s roadmap.<br />\n2. Python Refresher – ML Con
 text<br />\nVariables\, data types (int\, float\, string\, list\, tuple\, 
 dict).<br />\nLoops &amp\; conditionals.<br />\nFunctions (with arguments 
 &amp\; return values).<br />\nImporting libraries (import\, from ... impor
 t).<br />\n3. NumPy – Numerical Computing<br />\nWhat is NumPy and why i
 t’s used in ML.<br />\nCreating arrays (np.array\, np.zeros\, np.ones\, 
 np.arange\, np.linspace).<br />\nIndexing\, slicing\, reshaping.<br />\nAr
 ray operations (sum\, mean\, dot product).<br />\nBroadcasting.<br />\n<br
  />\n4. Pandas – Data Handling<br />\nWhat is a DataFrame and Series.<br
  />\nReading CSV/Excel files (pd.read_csv).<br />\nInspecting data (.head(
 )\, .info()\, .describe()).<br />\nSelecting rows &amp\; columns (loc\, il
 oc).<br />\nFiltering and conditional selection.<br />\nAdding\, renaming\
 , dropping columns.<br />\nHandling missing data (fillna\, dropna).<br />\
 n5. Pandas – Data Aggregation &amp\; Grouping<br />\nGrouping (groupby) 
 and aggregation (sum\, mean).<br />\nSorting data.<br />\nMerging &amp\; j
 oining datasets.<br />\nExporting cleaned data to CSV.<br />\n6. Matplotli
 b – Basic Visualization<br />\nLine plots\, bar charts\, scatter plots\,
  histograms.<br />\nAdding titles\, labels\, legends.<br />\nChanging colo
 rs\, styles\, and markers.<br />\n7. Seaborn – Statistical Visualization
 <br />\nWhy Seaborn is useful.<br />\nCommon plots:<br />\ncountplot (cate
 gorical count)<br />\nhistplot (distribution)<br />\nboxplot (outliers)<br
  />\nheatmap (correlation)<br />\n8. Putting It All Together – Mini Data
  Analysis Project<br />\nDataset: Iris or Student Scores dataset.<br />\n9
 . Wrap-up &amp\; Q&amp\;A<br />\nRecap of NumPy\, Pandas\, Matplotlib\, Se
 aborn.<br />\nShow how these same skills are used in real ML projects.<br 
 />\n<br />\n**NOTE: LAPTOP IS MANDATORY\, SINCE HANDS-ON SESSIONS NEEDS PR
 OGRAMMING</p>
URL:https://www.knowafest.com/python-for-machine-learning-workshop-2025
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