Dr. Rambabu Pemula

  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 – 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  

Machine Learning Introduction  Data Pre-processing Performance measurement of models  Supervised Learning  Decision Tree Learning  Unsupervised Learning  Ensemble Models 👉 Machine Learning -3 Syllabus Machine Learning Notes and PPTs👇 Unit1: Introduction and Data Preprocessing Notes Unit1: Introduction and Data Preprocessing PPTs UNIT 2 (A) Performance Measurement of Models Notes   UNIT 2 (A) Performance Measurement of Models PPTs UNIT 2(B): Supervised Learning Notes   UNIT 3(A) Supervised Learning Notes UNIT 3(A) Supervised Learning PPTs UNIT 3(B) Decision Tree Learning PPTs UNIT 4 : Unsupervised Learning PPTs UNIT 5: Ensemble Models Notes

  MACHINE LEARNING Syllabus: UNIT-1 Introduction: Brief Introduction to Machine Learning, Abstraction and Knowledge Representation, Types of Machine Learning Algorithms, Definition of learning systems, Goals and applications of machine learning, Aspects of developing a learning system, Data Types, training data, concept representation, function approximation. Data Pre-processing: Definition, Steps involved in pre-processing, Techniques UNIT-2 Performance measurement of models: Accuracy, Confusion matrix, TPR, FPR, FNR, TNR, Precision, recall, F1-score, Receiver Operating Characteristic Curve (ROC) curve and AUC. Supervised Learning1: Linear Regression, Multiple Variable Linear Regression, Naïve Bayes Classifiers, Gradient Descent, Multicollinearity, Bias-Variance trade-off. UNIT-3 Supervised Learning2 : Regularization, Logistic Regression, Squashing function, KNN, Support Vector Machine. Decision Tree Learning: Representing concepts as decision trees, Recursive induction of de...