Dr. Rambabu Pemula

  Unit – III Supervised Learning: Classification 1.     What is Classification 2.     General Approach to Classification 3.     K-Nearest Neighbour Algorithm 4.     Logistic Regression 5.     Decision Trees: a.      Construction b.     classification and regression trees c.      example 6.     Naive Bayesian Classifier 7.     Support Vector Machines: a.      Optimal Separation b.     Kernels c.      Algorithm 👉 UNIT-II (A) Supervised Learning Classification Notes 👉 UNIT-III (A) Supervised Learning Classification PPTs 👉 UNIT-III(A) Important Long Answer Questions 👉 UNIT-III (A) Supervised Learning: Classification Important Questions YouTube Link

  FUNDAMENTALS OF MACHINE LEARNING (Professional Elective - 3) Department : IT IV B.Tech I Semester Course Code Hours/Week Credits Marks A2275E9 L T P C CIE SEE Total 3 0 0 3 40 60 100   Course Outcomes: At the end of the course, the student should be able to 1.      Ability to identify machine learning problems corresponding to different applications . 2.      Ability to understand the basic concepts such as Decision trees and Neural Networks. 3.      Apply Machine Learning algorithms to solve problems of moderate complexity. 4.      Identify Machine Learning applications 5.      Understand Genetic algorithms and their applications Unit – I Introduc...

 Machine Learning Programs 👉 Data Preprocessing in Machine Learning 👉 Data Preprocessing in Machine learning (Handling Missing values ) 👉 Linear Regression - ML Program - Weight Prediction 👉 Naïve Bayes Classifier - ML Program 👉 LOGISTIC REGRESSION - PROGRAM 👉 KNN Machine Learning Program 👉 Support Vector Machine (SVM) - ML Program 👉 Decision Tree Classifier on Iris Dataset 👉 Classification of Iris flowers using Random Forest 👉 DBSCAN 👉 Implement and demonstrate the FIND-S algorithm for finding the most specific hypothesis based on a given set of training data samples. Read the training data from a .CSV file 👉 For a given set of training data examples stored in a .CSV file, implement and demonstrate the Candidate-Elimination algorithm to output a description of the set of all hypotheses consistent with the training examples. 👉 Write a program to demonstrate the working of the decision tree based ID3 algorithm. Use an appropriate data set for building the decision tree...

  Machine Learning MCQs 👉1.  Machine Learning MCQs - UNIT 1 👉2.  Machine Learning MCQs-2 (Performance Metrics, Linear Regression, Naïve Bayes Classifier ) 👉3.  Machine Learning MCQs-3 (Logistic Regression, KNN, SVM, Decision Tree) 👉4.  Machine Learning MCQs - 4 (Clustering, Dimensionality Reduction) 👉5. Machine Learning MCQs - 5 (Ensemble Models)

  Decision Trees Characteristics Context: Decision Trees are a fundamental machine learning algorithm used for both classification and regression tasks. Understanding their characteristics, capabilities, and limitations is crucial for effectively applying them to solve real-world problems. Question: Which of the following statements are true regarding the properties and behavior of Decision Trees? Statements to Evaluate: 1. Decision tree makes no assumptions about the data. 2. The decision tree model can learn non-linear decision boundaries. 3. Decision trees cannot explain how the target will change if a variable is changed by 1 unit (marginal effect). 4. Hyperparameter tuning is not required in decision trees. 5. In a decision tree, increasing entropy implies increasing purity. 6. In a decision tree, the entropy of a node decreases as we go down the decision tree. Choose the correct answer from below : A) 1, 2, and 5 B) 3, 5 and 6 C) 2, 3, 4 and 5 D) 1,2,3 and 6 Ans: D 1, 2, 3 an...

  Decision Tree MCQs 1.      Decision Trees can be used for A.     Classification Tasks B.     Regression Tasks C.     Multi-output tasks D.     All of the above Ans: D   2.      The iris dataset has A.     5 features and 3 classes B.     4 features and 3 classes C.     2 features and 3 classes D.     4 features and 2 classes Ans: B   3.      A node’s value attribute tells you how many training instances of each class this node applies to Top of Form  True  False Ans: A 4.      A node’s gini attribute measures Top of Form  The number of training instances in the node  The ratio of training instances in the node  Its impurity None of these Ans: C 5.      I...