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Unit 1 :- Introduction to Artificial Intelligence
Introduction of AI, Applications of AI and its Examples, Machine Learning .
Unit 2 : - AI Concepts, Terminology, and Application Areas
Basic AI Concepts , Understand How AI learns, Concepts of AI, Applications of Artificial Intelligence –
Examples, Three Stages of Artificial Intelligence, Image Recognition, Effects of Artificial Intelligence on
Society, Supervised Learning for Telemedicine, Solves Complex Social Problems, Benefits Multiple
Industries
Unit 3 :- AI: Issues, Ethical Considerations and Concerns
Issues and concerns surrounding AI, Including - ethical considerations, bias, jobs, etc. - their impact on
society, costs and importance of AI.
Unit 4 :- Drafting a Methods and Results section
Relationship between Machine Learning and Statistical Analysis, Process of Machine Learning, Types of
Machine Learning, Meaning of Unsupervised Learning, Meaning of Semi-supervised Learning, Algorithms
of Machine Learning , Regression, Naive Bayes, Naive Bayes Classification, Machine Learning Algorithms,
Deep Learning, Artificial Neural Network Definition, Definition of Perceptron, Online and Batch Learning.
Unit 5 :- Machine Learning Workflow
Learning Objective, Machine Learning Workflow, Get more data, Ask a Sharp Question, Add Data to the
Table, Check for Quality, Transform Features, Answer the Questions, Use the Answer.
Unit 6 :- Performance Metrics
Performance Metrics, Need For Performance Metrics, Key Methods Of Performance Metrics, Confusion Matrix
Example, Terms Of Confusion Matrix, Minimize False Cases, Minimize False Positive Example, Accuracy, Precision,
Recall Or Sensitivity, Specificity, F1 Score.
Unit 7 :- The Future with AI, and AI in Action
Current thinking on future with AI, Career in AI. Show AI in action by using Computer Vision to classify
images.
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