UnderStand Classification Algorithm,Supervised Learning,KNN Classification Algorithm

Опубликовано: 24 Февраль 2023
на канале: Abhay More

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🎉 Welcome to the Business Intelligence and Data Analytics (BIDA) Practical Series by MORE Academy! 🎓

TY BSc IT Sem 6 (2026) – Business Intelligence Practical 3
In this detailed session, you’ll learn the concepts of Classification Algorithms, Supervised Learning, and K-Nearest Neighbour (KNN) Algorithm — important topics under the Mumbai University BSc IT Semester VI Business Intelligence syllabus.

This video gives you a clear conceptual understanding of how machine learning techniques like classification help in data-driven decision-making within the BI domain.

📘 Practical Details
Course: BSc IT
Semester: VI
Subject: Business Intelligence
Practical No.: 3
Title: Perform the Data Classification Using Classification Algorithm

⏱ Timecodes:
0:00 - Intro
0:27 — Supervised Machine Learning: An Example
1:00 — Machine Learning and Our Focus
1:18 — Data & Goal
2:45 — Supervised vs Unsupervised Learning
3:00 — Supervised Learning Process: An Example
4:23 — Classification Algorithms
5:10 — K-Nearest Neighbours (KNN)

📘 What You Will Learn Today
✅ What is Supervised Learning in Machine Learning
✅ Difference between Supervised and Unsupervised Learning
✅ How Classification Algorithms work in BI
✅ Understanding the KNN (K-Nearest Neighbour) Algorithm
✅ Real-world examples of classification in analytics
✅ How data classification supports Business Intelligence

⚙️ Step-by-Step Concept + Practical Explanation
All your doubts related to classification algorithms and supervised learning are clearly solved in this session!

📚 What MORE Academy Offers:

🎓 Academic Support
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✅ Documentation & correction
✅ Technical implementation
✅ Presentation & viva preparation

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We also provide study abroad assistance — from course selection to university application support.

👩‍💻 Why Watch This Video?
✅ Understand classification algorithms with real-world examples
✅ Learn supervised vs unsupervised learning clearly
✅ Build a strong foundation for BI practicals and ML-based concepts
✅ Prepare for practical exams and placement-level discussions

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