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Class 10th Artificial Intelligence Book – Full Video Course

Vardan Garg

This Course Covers The Following:

  • CBSE Syllabus Based Topic Wise Explanation
  • Syllabus Overview of the Subject
  • Chapter wise Summary
  • Complete Solutions
  • Full Chapter Videos
  • One Shot Revision
  • MCQs

This course covers the complete CBSE course based syllabus with all chapters of Class 10th Artificial Intelligence which has been taught topic wise. This also covers the concepts useful for competitive exams.

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Artificial Intelligence 1 Lectures
  • Syllabus OverviewSyllabus Overview
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    6:05 Duration
Theory : Unit 1 - Introduction to AI 10 Lectures
  • 1.1 Intelligence and Its Traits1.1 Intelligence and Its Traits
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    12:22 Duration
  • 1.2 Decision Making1.2 Decision Making
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    10:47 Duration
  • 1.3 Defining AI (Various Definitions)1.3 Defining AI (Various Definitions)
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    6:25 Duration
  • 1.4 How do Machines Become Artificially Intelligent?1.4 How do Machines Become Artificially Intelligent?
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    8:00 Duration
  • 1.5 What Is AI / What Is Not AI?1.5 What Is AI / What Is Not AI?
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    17:48 Duration
  • 5 More Lessons
Theory : Unit 2 - AI Project Cycle 19 Lectures
  • 2.1 Overview2.1 Overview
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    38:15 Duration
  • 2.2 Problem Scoping: Schema2.2 Problem Scoping: Schema
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    23:01 Duration
  • 2.3 Problem Scoping2.3 Problem Scoping
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    23:01 Duration
  • 2.4 Data Acquisition: What is Data?2.4 Data Acquisition: What is Data?
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    41:54 Duration
  • 2.5 Data Acquisition: Acquiring Data & System Maps2.5 Data Acquisition: Acquiring Data & System Maps
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    46:36 Duration
  • 14 More Lessons
Practical : Unit 3 - Recap: What is Programming? 32 Lectures
  • 3.1 Advance Python3.1 Advance Python
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    12:43 Duration
  • 3.2 Advance Python - Recap: Need of Programming Language3.2 Advance Python - Recap: Need of Programming Language
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    17:35 Duration
  • 3.3 Advance Python - Recap: Why Python?3.3 Advance Python - Recap: Why Python?
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    17:59 Duration
  • 3.4 Advance Python - Recap: Installing Python for Windows3.4 Advance Python - Recap: Installing Python for Windows
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    21:40 Duration
  • 3.5 Advance Python - Recap: IDE and Its Installation3.5 Advance Python - Recap: IDE and Its Installation
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    39:54 Duration
  • 27 More Lessons
Theory : Unit 4 - Data Science 35 Lectures
  • 4.1 Chapter Overview4.1 Chapter Overview
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    15:28 Duration
  • 4.2 Introduction4.2 Introduction
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    32:30 Duration
  • 4.3 Applications of Data Science (Part 1)4.3 Applications of Data Science (Part 1)
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    44:56 Duration
  • 4.4 Applications of Data Science (Part 2)4.4 Applications of Data Science (Part 2)
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    48:42 Duration
  • 4.5 Revisiting AI Project Cycle4.5 Revisiting AI Project Cycle
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    9:35 Duration
  • 30 More Lessons
Theory : Unit 5 - Computer Vision 74 Lectures
  • 5.1 Overview5.1 Overview
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    23:42 Duration
  • 5.2 Introduction to Computer Vision5.2 Introduction to Computer Vision
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    27:12 Duration
  • 5.3 Computer Vision Vs Human Vision5.3 Computer Vision Vs Human Vision
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    23:32 Duration
  • 5.4 Applications of Computer Vision (Part 1)5.4 Applications of Computer Vision (Part 1)
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    1:34:49 Duration
  • 5.5 Applications of Computer Vision (Part 2)5.5 Applications of Computer Vision (Part 2)
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    1:17:48 Duration
  • 69 More Lessons
Theory : Unit 6 - Natural Language Processing 24 Lectures
  • 6.1 Overview6.1 Overview
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    14:21 Duration
  • 6.2 Introduction6.2 Introduction
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    26:05 Duration
  • 6.3 Applications of Natural Language Processing (Part 1)6.3 Applications of Natural Language Processing (Part 1)
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    19:07 Duration
  • 6.4 Applications of Natural Language Processing (Part 2)6.4 Applications of Natural Language Processing (Part 2)
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    34:00 Duration
  • 6.5 Categories of Natural Language Processing6.5 Categories of Natural Language Processing
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    18:08 Duration
  • 19 More Lessons
Theory : Unit 7 - Evaluation 11 Lectures
  • 7.1 Overview7.1 Overview
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    6:41 Duration
  • 7.2 Introduction to Evaluation7.2 Introduction to Evaluation
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    23:23 Duration
  • 7.3 Model Evaluation Terminologies - TP, TN, FP, FN7.3 Model Evaluation Terminologies - TP, TN, FP, FN
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    16:50 Duration
  • 7.4 Model Evaluation Terminologies - Confusion Matrix7.4 Model Evaluation Terminologies - Confusion Matrix
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    9:48 Duration
  • 7.5 Evaluation Methods - Accuracy7.5 Evaluation Methods - Accuracy
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    25:26 Duration
  • 6 More Lessons
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