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

  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...

  ML: Intro to Machine Learning Q1.  Applications of the Supervised Learning Select the problem statements where you can apply supervised algorithms. 1.      For an e-commerce website, segmenting the unlabelled customers based on their behaviour from a large dataset. 2.      Given data on crop yields over the last 50 years, trying to predict next year's crop yields. 3.      Based on data samples of webpages, classifying a webpage whether the content on the web page should be considered "child friendly" or "adult". 4.      Given a large dataset of medical records from patients suffering from heart disease, try to learn whether there might be different groups of such patients.   Ans: Correct Answer: Given data on crop yields over the last 50 years, trying to predict next year’s crop yields. Based on data samples of webpages, classifying a webpage whether the content...