JNTUK B.Tech 3-2 R20 Machine Learning Unit wise Questions


  JNTUK B.Tech 3-2 R20 Machine Learning Unit wise Questions for your both internal and external examinations are now available. These questions are prepared by at most care according to regulation and syllabus. Bu preparing these questions you can get good marks in your external examinations.


  1. Can you name four of the main challenges in Machine Learning?
  2. What are different types of machine learning systems.
  3. Write short note on AI, ML, & DL.
  4. Write the importance of statistics in Supervise learning and unsupervised learning.
  5. Write a note on Training loss Vs Testing loss
  6. Write about different risk statistics that you need to encounter while working with Machine Learning. 
  7. What are different Tradeoffs in Statistical Learning? Explain.
  8. How to estimate risk statistics? how to Minimize Empirical Risk ? Explain.
  9. Write down the procedure for estimating sampling distribution of an estimator .


  1. Write a shot note of various Distance based Methods of classification / regression.
  2. With an example, explain KNN.
  3. What is decision tree? Explain the procedure to construct decision tree.
  4. What are the appropriate problems for decision tree learning.
  5. How to identify best splitting attribute in decision tee construction?
  6. Explain Naive Bayes classification with example.
  7. Explain linear and logistic regression with examples.
  8. With an example explain binary classification in machine learning.


  1. What do you mean by Ensemble learning? What are its main challenges for developing?
  2. What is the difference between hard and soft voting classifiers?
  3. Differences between bagging and Boosting 
  4. What is the benefit of out-of-bag evaluation?
  5. Explain about AdaBoost ensemble and Gradient Boosting ensemble.
  6. With an example, explain the working of random forest.
  7.  Differences between decision tree and random forest.
  8. What is stacking? Explain working of stacking as Ensemble learning?
  9. What are SVM? Explain linear and non-linear SVM
  10. Write a note on SVM Regression.
  11. Write about Naive Bayes classifiers Vs  SVM in Text classification.


  1. With an example, explain K-means clustering. Also write limitations of K-means.
  2. How clustering is used in image segmentation, preprocessing, and sem-supervised learning?
  3. Write about DBSCAN and Gaussian Mixtures.
  4. What do you mean by cure of dimensionality? What solutions do you propose for this?
  5. Write down the Main Approaches for Dimensionality Reduction
  6. What is PCA? How it works as Dimensionality Reduction technique? Explain with example.
  7. How to implement PCA using Sci-kit learn?
  8. Write a note on Randomized PCA and Kernel PCA


  1. Write down the biological motivation behind ANN
  2. What is Perception? Explain Perception training with example.
  3. What is gradient descent? Derive delta rules with algorithm.
  4. Write a short note on Multi-layer networks and back propagation. 
  5. What are the various ways to implement MLPs with Keras? Explain.
  6. Write about different ways to Installing TensorFlow 2.
  7. With an example program, explain the procedure of Loading and Preprocessing Data with TensorFlow.


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