Machine Learning

  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 decisi

  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 on the web page should be considered “child friendly” or “adult”