Dr. Rambabu Pemula

  WEEK 9:      Write a program to implement SVM algorithm to classify the iris data set. Print both correct and wrong predictions. 👉 Student Handout 👉 Program 👉 Colab 👉 YouTube Link

  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...

  ESSENTIALS OF MACHINE LEARNING Unit – I:  Introduction to Machine Learning What is machine learning, types of machine learning, applications of machine learning, preparing to model, modeling, and evaluation        👉 UNIT-1 (A) Introduction to Machine Learning Notes 👉 UNIT-1 (A) Introduction to Machine Learning PPTs 👉 UNIT-1 (B) Introduction to Machine Learning Notes 👉 UNIT-1 (B) Introduction to Machine Learning PPTs Unit – II:  Supervised Learning: Regression Introduction to Regression, Example of Regression, Simple Linear Regression, Multiple Linear Regression, Assumptions in Regression Analysis, Improving the accuracy of the Linear Regression Model, Ridge Regression, and Lasso Regression.         👉 UNIT-II Supervised Learning: Regression Notes  👉 UNIT-II Supervised Learning: Regression PPTs Unit – III:  Supervised Learning: Classification What is Classification, General Approach to Classification, K-...

Unit – V Ensemble Learning and Reinforcement Learning 1.      Ensemble Methods: a.       Bagging b.      Boosting  c.       Random Forests 2.      Reinforcement Learning: a.       Overview b.      Example c.       Getting Lost d.      Markov Decision Process e.       Values f.       difference between SARSA and Q Learning g.      Uses of Reinforcement Learning  

  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).

  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

  Unit – II Supervised Learning: Regression 1.     Introduction to Regression 2.     Example of Regression 3.     Simple Linear Regression 4.     Multiple Linear Regression 5.     Assumptions in Regression Analysis 6.     Improving the accuracy of the Linear Regression Model 7.     Ridge Regression 8.     Lasso Regression 👉 UNIT-II Supervised Learning: Regression Notes 👉 UNIT-II Supervised Learning: Regression PPTs