AI Project Framework | One Shot | ICSE Class 10 Robotics & AI Chapter 8 | Full Syllabus Code 66

Master the AI Project Framework in this comprehensive one-shot lesson for ICSE Class 10 Robotics and AI (Subject Code 66)! 🤖🔍

In this session, we dive deep into Chapter 8, exploring the systematic four-step process used to solve real-world problems using Artificial Intelligence. Think of this video as your AI Detective’s Handbook—we’ll learn how to ask the right questions, gather evidence, find patterns, and build a working AI model.

What you will learn in this lesson:

✅ The 4-Step AI Framework: Problem Scoping, Data Acquisition, Data Exploration, and Modeling/Evaluation.

✅ Problem Scoping: Using the 4 W’s (Who, What, Where, Why) to define your project clearly.

✅ Data Acquisition: Understanding datasets, data features, and the difference between Training vs. Testing Data.

✅ Data Exploration: How to use the "Investigator's Toolkit" (Bar Graphs, Scatter Plots, and Histograms) to spot trends and outliers.

✅ Modeling & Evaluation: Comparing Supervised vs. Unsupervised Learning and measuring success through accuracy metrics.

Why watch this?
Strictly follows the ICSE Grade 10 Syllabus.
Exam-Focused: Brief, clear explanations designed for quick revision and deep conceptual clarity.
Real-World Case Study: Follow our "Late Bus Prediction" mystery to see how AI is applied in a school setting.

Subscribe to Prof. Analysis for the complete ICSE Class 10 Robotics & AI playlist, including Part 1 (Robotics) and Part 2 (Artificial Intelligence)!

🕒 Timestamps
[00:00] Introduction: ICSE Class 10 Robotics & AI Syllabus Overview
[01:25] The AI Detective Handbook: The 4-Step Framework
[03:01] Step 1: Problem Scoping (Asking the Right Questions)
[04:38] The 4 W’s Detective Framework (Who, What, Where, Why)
[06:18] Step 2: Data Acquisition (Gathering the Evidence)
[07:36] Key Terminology: Data Features, Datasets & Evidence Lockers
[08:39] Training Data vs. Testing Data Explained
[09:33] Step 3: Data Exploration (Connecting the Clues)
[10:46] Visualizing Data: Bar Graphs, Scatter Plots & Pie Charts
[13:40] Finding Patterns: How Weather and Time Affect Delays
[14:22] Step 4: Modeling (Building the Theory)
[15:32] Rule-Based vs. Learning-Based Approaches
[17:19] Supervised vs. Unsupervised Learning Modes
[19:12] Evaluation: Testing Accuracy & Final Verdict
[20:23] Summary: The Complete Investigation Flow
[22:01] Conclusion: Starting Your Own AI Project

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