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R_23 - Machine Learning - Important Questions Unit wise

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  5 Marks questions UNIT–I Introduction to Machine Learning Most Important 5-Mark Questions Define Machine Learning and explain its applications . Explain the evolution of Machine Learning with examples. Explain learning by rote, learning by induction, and reinforcement learning . Describe the stages in the Machine Learning process with a neat diagram. Explain data acquisition and list various data sources . What is feature engineering ? Explain its importance in ML. Explain different types of data used in Machine Learning. Describe data representation techniques in ML. Explain model selection and model learning . Explain model evaluation techniques and accuracy testing. What is search and learning in Machine Learning? Explain the concept of datasets (training, testing, validation). Very High Probability : Stages of ML, Feature Engineering, Model Evaluation, Data Acquisition ...
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R_23 : Machine Learning - Unit wise topics (Very Important, Important, Tips)

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  UNIT–I: Introduction to Machine Learning Very Important Machine Learning definition & types (Supervised vs Unsupervised) Learning paradigms: Learning by Rote Learning by Induction Reinforcement Learning Stages in Machine Learning Pipeline Feature Engineering & Data Representation Model Evaluation techniques (Accuracy, Precision, Recall basics) Important Types of Data (Structured, Unstructured) Model Selection vs Model Learning Training vs Testing vs Validation Quick Topics Data acquisition basics Data sets (training/test split) Exam Tip:  Expect 2–5 mark theory questions + 10 mark ML pipeline diagram UNIT–II: KNN & Distance-Based Models Very Important K-Nearest Neighbor (KNN) Algorithm (FULL) Steps of algorithm Numerical problems (very important 🔥) Distance Measures Euclidean Manhattan Minkowski KNN for Classification vs Regression Important Radius-based nearest neighbor Performance of cl...
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Machine Learning - R_23 - JNTUK, Model Paper - 3

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JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY KAKINADA B.Tech III Year II Semester – R23 Course: MACHINE LEARNING Time: 3 Hours Max Marks: 70 SECTION – I (50 Marks) Answer ALL questions (Each question carries 10 marks: a) 5 + b) 5) UNIT – I : Introduction to Machine Learning 1. a) Explain the Evolution of Machine Learning. Highlight major milestones and applications. (5M) b) Explain the Machine Learning system architecture and clearly describe the role of each stage. (5M) 2. a) Explain Data Sets in Machine Learning. Discuss training, validation, and test datasets. (5M) b) Explain Search and Learning in Machine Learning with a suitable example. (5M) UNIT – II : Nearest Neighbor–Based Models 3. a) Explain Distance Measures used in ML. Compare Euclidean, Manhattan, and Minkowski distances. (5M) b) Apply Radius Nearest Neighbor algorithm to classify a test instance. (5M) 4. a) Explain Proximity between binary patterns with suitable meas...
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