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 – II: Decision Tree learning and Neural Network learning Decision Tree learning 1.      DecisionTree representation and learning algorithm, 2.      appropriate problems for Decision Tree Learning, 3.      Hypothesisspace search in Decision Tree Learning, 4.      Inductive bias in Decision Tree Learning: Occam’s razor, 5.      Issues in Decision Tree Learning. Introduction to Neural Network Learning , 1.      Introduction to Perceptions, 2.      Perceptions, 3.      Introduction to Gradient Descent 4.      Back propagation. 👉 UNIT-2 (A) Decision Tree Learning Notes   👉 UNIT-2 (A) Decision Tree Learning PPTs 👉 UNIT-2(A) Decision Tree Learning Long Answer Questions 👉 UNIT-2 (A) Decision Tree Long Answer Questions YouTube Link 👉 UNIT-2 (B) Introduction to Neural Network Lear...

Machine Learning  Introduction Concept Learning and the General to Specific Ordering Decision Tree Learning Artificial Neural Networks Bayesian Learning Instance-Based Learning Genetic Algorithms Learning Sets of Rules Analytical Learning Reinforcement Learning 👉 Machine Learning 2 Syllabus UNIT-1 :  Introduction &  Concept Learning and the General to Specific Ordering Introduction - Well-Posed Learning Problems, Designing a Learning System, Perspectives and Issues in Machine Learning, Introduction to Supervised, Unsupervised and Reinforcement Learning. Concept Learning and the General to Specific Ordering – Introduction, A Concept Learning Task, Concept Learning as Search, Find-S: Finding a Maximally Specific Hypothesis, Version Spaces and the Candidate Elimination Algorithm. 👉 Machine Learning 2- UNIT-1 (A) Notes: Introduction & Concept Learning and the General to Specific Ordering Notes 👉 Machine Learning 2 UNIT-1 (A) PPTs: Introduction PPTs 👉 Machine L...

  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...