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Course Content
Syllabus
Course Objective: 1. Equip students with foundational knowledge in AI & ML, including understanding technology landscape, tools/platforms for deployment, data importance, statistical foundations, and data visualization for effective decision-making. 2. Essential concepts in machine learning, including supervised and unsupervised learning, ensemble techniques, recommendation systems, reinforcement learning, and their applications. 3. Equip students with a comprehensive understanding of key machine learning algorithms and techniques, including linear regression, logistic regression, decision trees, random forests, KNN, neural networks, deep learning, visualization, NLP, and text analytics. 4. Prepare students to lead digital transformation initiatives by developing AI & ML strategies aligned with organizational goals, ensuring business model fit, ROI, success measures, and addressing data requirements. 5. Equip management students with essential visualization techniques, emphasizing context, storytelling, and utilizing data to influence decision-making effectively.
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UNIT-I TECHNOLOGY OVERVIEW AND FUNDAMENTALS
UNIT-II UNBOXING ML AND ITS APPLICATIONS
UNIT-III UNBOXING AI AND ITS APPLICATIONS
UNIT-IV AI & ML FOR BUSINESS EXCELLENCE
UNIT-V FRAMEWORKS FOR DELIVERING AI SOLUTIONS
TEXT BOOKS & REFERENCE BOOKS
LECTURE NOTES
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Question Bank
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Puzzles
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23BAT615 & Artificial Intelligence for Managers

Visualisation techniques – Importance of Context – Lessons in Story telling – Usage of Data  to influence decisions -Basic Visualisation Skill

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