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 – IV Unsupervised Learning and Dimensionality Reduction 1.     Types of Unsupervised Learning, 2.     Challenges in Unsupervised Learning, 3.     Clustering Algorithms: a.      K-Means, b.     Agglomerative, c.      DBSCAN, d.     Comparing and Evaluating Clustering Algorithms. 4.     Dimensionality Reduction: a.      Linear Discriminant Analysis (LDA), b.     Principal Component Analysis (PCA), c.      Factor Analysis (FA).