Dr. Rambabu Pemula

  MACHINE LEARNING USING PYTHON LAB Course Outcomes: At the end of the course the student should be able to: 1.      Select data, model selection, model complexity and identify the trends. 2.      Understand a range of machine learning algorithms along with their strengths and weaknesses. 3.      Build predictive models from data and analyze their performance. List of Experiments WEEK 1 : a)      Write a program using scikit-learn to implement K-means Clustering b)      Program to calculate the entropy and the information gain c)      Program to implement perceptron. WEEK 2: ·        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. WEEK 3: ·        Fo...

  Unit – I: Introduction and Concept Learning Introduction 1.      Well-posed learning problems, 2.      D esigning a learning system 3.      Perspectives issues in machine learning, 4.      Types of learning. Concept Learning 5.      Concept learning task, 6.      Concept Learning as search through a hypothesis space, 7.      Findingmaximally specific hypotheses (FIND-S) 8.      Versionspaces and the candidate elimination algorithm, 9.      Inductive Bias 👉 UNIT-1 (A) Introduction Notes   👉 UNIT-1 (A) Introduction PPTs 👉 UNIT-1(B) Concept Learning PPTs 👉 UNIT-1 Introduction and Concept Learning Notes 👉 UNIT-1 Introduction and Concept Learning Long Answer Question  

WEEK 3: • 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.