Syllabus Overview1.1 Intelligence and Its Traits1.2 Decision Making1.3 Defining AI (Various Definitions)1.4 How do Machines Become Artificially Intelligent?1.5 What Is AI / What Is Not AI?2.1 Overview2.2 Problem Scoping: Schema2.3 Problem Scoping2.4 Data Acquisition: What is Data?2.5 Data Acquisition: Acquiring Data & System Maps3.1 Advance Python3.2 Advance Python - Recap: Need of Programming Language3.3 Advance Python - Recap: Why Python?3.4 Advance Python - Recap: Installing Python for Windows3.5 Advance Python - Recap: IDE and Its Installation4.1 Chapter Overview4.2 Introduction4.3 Applications of Data Science (Part 1)4.4 Applications of Data Science (Part 2)4.5 Revisiting AI Project Cycle5.1 Overview5.2 Introduction to Computer Vision5.3 Computer Vision Vs Human Vision5.4 Applications of Computer Vision (Part 1)5.5 Applications of Computer Vision (Part 2)6.1 Overview6.2 Introduction6.3 Applications of Natural Language Processing (Part 1)6.4 Applications of Natural Language Processing (Part 2)6.5 Categories of Natural Language Processing7.1 Overview7.2 Introduction to Evaluation7.3 Model Evaluation Terminologies - TP, TN, FP, FN7.4 Model Evaluation Terminologies - Confusion Matrix7.5 Evaluation Methods - Accuracy
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