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 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20240317T131746Z UID:2F650901-9C8B-41A2-89A7-C04DA9F41C10 DTSTART;TZID=Asia/Kolkata:20240315T090000 DTEND;TZID=Asia/Kolkata:20240315T150000 DESCRIPTION:"Deep Dive Into Dimensionality Reduction - A Machine Learning A pproach" is a one day workshop is aimed to provide students from 6th Semes ter of CE & IT Department with an in-depth understanding of machine learni ng techniques\, focusing particularly on dimensionality reduction methods. \n\nIt is led by Prof. (Dr.) Mehul Raval\, Experiential Learning\, Ahemeda bad University who will be covering key topics such as Machine Learning fu ndamentals\, exploration of problem areas in Regression & Classification\, and comprehensive insights into Principal Component Analysis (PCA). These topics are crucial for comprehending complex data structures and are fund amental in various applications across industries.\n\nIn an era dominated by data-driven decision-making\, proficiency in machine learning and dimen sionality reduction techniques is indispensable for aspiring data scientis ts and machine learning engineers. Through interactive sessions\, practica l exercises\, and collaborative discussions\, students will enhance their analytical capabilities and expand their technical expertise under the gui dance of experienced professionals in the field.\n\nIn conclusion\, this w orkshop offers students a chance to deepen their understanding of machine learning\, particularly in dimensionality reduction. Through the interacti ve session and practical exercises\, students will enhance their analytica l skills. Mastering key topics like Machine Learning fundamentals and PCA promises to unlock new opportunities in data science careers.\n\nCo-sponso red by: IEEE LDRP Institute of Technology & Research Student Branch\n\nSpe aker(s): Dr. Mehul Raval\n\nRoom: MCA Labs\, Bldg: Shift 1\, LDRP ITR Camp us\, Gandhinagar\, Gujarat\, Gujarat\, India\, 382016 LOCATION:Room: MCA Labs\, Bldg: Shift 1\, LDRP ITR Campus\, Gandhinagar\, G ujarat\, Gujarat\, India\, 382016 ORGANIZER:ashish_ce@ldrp.ac.in SEQUENCE:47 SUMMARY:Deep Dive Into Dimensionality Reduction - A Machine Learning Approa ch URL;VALUE=URI:https://events.vtools.ieee.org/m/411165 X-ALT-DESC:Description: <br /><p>"Deep Dive Into Dimensionality Reduction - A Machine Learning Approach" is a one day workshop is aimed to provide st udents from 6th Semester of CE &amp\; IT Department with an in-depth under standing of machine learning techniques\, focusing particularly on dimensi onality reduction methods.</p>\n<p>It is led by Prof. (Dr.) Mehul Raval\, Experiential Learning\, Ahemedabad University who will be covering key top ics such as Machine Learning fundamentals\, exploration of problem areas i n Regression &amp\; Classification\, and comprehensive insights into Princ ipal Component Analysis (PCA). These topics are crucial for comprehending complex data structures and are fundamental in various applications across industries.</p>\n<p>In an era dominated by data-driven decision-making\, proficiency in machine learning and dimensionality reduction techniques is indispensable for aspiring data scientists and machine learning engineers . Through interactive sessions\, practical exercises\, and collaborative d iscussions\, students&nbsp\;will enhance their analytical capabilities and expand their technical expertise under the guidance of experienced profes sionals in the field.</p>\n<p>In conclusion\, this workshop offers student s a chance to deepen their understanding of machine learning\, particularl y in dimensionality reduction. Through the interactive session and practic al exercises\, students will enhance their analytical skills. Mastering ke y topics like Machine Learning fundamentals and PCA promises to unlock new opportunities in data science careers.</p>\n<p>&nbsp\;</p> END:VEVENT END:VCALENDAR