Dr. Rambabu Pemula

  Unit – III Supervised Learning: Classification 1.     What is Classification 2.     General Approach to Classification 3.     K-NearestNeighbour 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

  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 👉 UNIT-2 Important Long Answer Questions 👉 UNIT-2 Supervised Learning: Regression Important Questions YouTube Link

  Unit – I: Introduction to Machine Learning 1.      What is Machine Learning 2.      Types of Machine Learning 3.      Applications of Machine learning 4.      Preparing to Model 5.      Modelling 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-1 Important Long Answer Questions 👉 UNIT-1 Introduction to Machine Learning Important Question YouTube Link

  ESSENTIALS OF MACHINE LEARNING Course Outcomes: At the end of this course, students will be able to: 1. Understand the Concepts of Machine Learning. 2. Develop Simple Regression Models. 3. Build various classification algorithms. 4. Analyse the need of ensemble learning and dimension reduction. 5. Apply the Clustering algorithms for developing applications. Unit – I:  Introduction to Machine Learning What is Machine Learning, Types of Machine Learning, Applications of Machine learning, preparing to Model, Modelling and Evaluation 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, Lasso Regression. Unit – III:  Supervised Learning: Classification What is Classification, General Approach to Classification, K-Nearest Neighbour Algorithm, L...

  FUNDAMENTALS OF MACHINE LEARNING Unit – I Introduction andConcept Learning: Well-posed learning problems, designing a learning system Perspectives and issues in machine learning, Types of learning.  Concept learning task , Concept Learning as search through a hypothesis space, Finding maximally specific hypotheses, Version spaces and the candidate elimination algorithm, Inductive Bias 👉 UNIT-1 (A) Introduction Notes   👉 UNIT-1(B) Concept Learning PPTs 👉 UNIT-1 Introduction and Concept Learning Notes 👉 UNIT-1 Introduction and Concept Learning Long Answer Question 👉 UNIT-1 Introduction & Concept Learning Important Questions YouTube Link 👉 Fundamentals of Machine Learning YouTube Videos Links Unit - II Decision Treelearning and Neural Network learning : Decision Tree representation and learning algorithm, appropriate problems for Decision Tree Learning, Hypothesis space search in Decision Tree Learning, Inductive bias in Decision Tree Learning: Occam’s razor,...

  Unit – V: Genetic Algorithm Genetic Algorithm : 1.      Biological Motivation 2.      Representing Hypothesis 3.      Genetic Operators 4.      Fitness Function and Selection 5.      Hypothesis Space Search 6.      Genetic Programming 7.      Models of Evolution and Learning 8.      Parallelizing Genetic Algorithms

  Unit – IV: Instance-Based Techniques Instance-based Techniques : 1.      Introduction to Instance-Based Techniques 2.      Lazy vs. Eager Generalization 3.      K-Nearest Neighbor 4.      Radial Basis Function 5.      Case Based Reasoning 6.      Remarks on Lazy vs Eager Learning