Machine Learning

                                                                                            UNIT 5 (B)                                                                       Clustering 5.      How does clustering work 6.      finding similarities using distances 7.      Euclidean distance and other distance metrics k-Means Clustering: 8.      Plotting customers with their segments 9.      normalizing features 10.   cluster centres and interpreting the Clusters 11.   Hierarchical Clustering   UNIT-5 (B) Long Answer Questions 1.      5.      Explain Hierarchical Clustering algorithm with example. 6.      What is the purpose of normalizing features?  How it can perform? Explain. 7.      How can you find the similarities using distances? List the different distance metrics. 8.      How can you be plotting the customers with their segments? Explain.   Short Answer Questions 1.   4.      What is the purpose of cluster centre? 5.      How can you d

   Unit 5 B Clustering How does clustering work finding similarities using distances Euclidean distance and other distance metrics k-Means Clustering: Plotting customers with their segments normalizing features cluster centres and interpreting the Clusters   Hierarchical Clustering ------------------------------------------------------------------------------------------------------------------------

  Unit 5 B Clustering 5.      How does clustering work 6.      finding similarities using distances 7.      Euclidean distance and other distance metrics k-Means Clustering: 8.      Plotting customers with their segments 9.      normalizing features 10.   cluster centres and interpreting the Clusters 11.   Hierarchical Clustering -------------------------------------------------------------------------------------------------------------------------

  Machine Learning The Machine Learning Landscape Classification Support Vector Machines Decision Trees Ensemble Learning and Random Forests Dimensionality Reduction Clustering 👉  Machine Learning 1 Syllabus Unit I: The Machine Learning Landscape : What Is Machine Learning? Why Use Machine Learning? Types of Machine Learning Systems , Supervised /Unsupervised Learning, Batch and Online Learning, Instance-Based Versus Model-Based Learning, Main Challenges of Machine Learning , Insufficient Quantity of Training Data, Nonrepresentative Training Data, Poor-Quality Data, Irrelevant Features, Overfitting the Training Data, Underfitting the Training Data, Stepping Back, Testing and Validating. 👉 UNIT 1(A) NOTEs : The Machine Learning Landscape Notes 👉 UNIT 1(A) PPTs: The Machine Learning Landscape 👉 UNIT 1(B) NOTEs: The Machine Learning Landscape NOTEs 👉 Machine Learning 1 : UNIT 1(B) PPTs: The Machine Learning Landscape PPTs 👉 Machine Learning 1: UNIT 1 The Machine Learning Landscape

Machine Learning Syllabus  Unit I: The Machine Learning Landscape : What Is Machine Learning? Why Use Machine Learning? Types of Machine Learning Systems, Supervised/Unsupervised Learning, Batch and Online Learning, Instance-Based Versus Model-Based Learning, Main Challenges of Machine Learning, Insufficient Quantity of Training Data, Nonrepresentative Training Data, Poor-Quality Data, Irrelevant Features, Overfitting the Training Data, Underfitting the Training Data, Stepping Back, Testing and Validating. Unit II: Classification: Training a Binary Classifier, Performance Measures, Measuring Accuracy Using Cross-Validation, Confusion Matrix, Precision and Recall, Precision/Recall Tradeoff, The ROC Curve, Multiclass Classification, Error Analysis, Multilabel Classification, Multi Output Classification. k-NN Classifier. Unit III: Support Vector Machines: Linear SVM Classification, Soft Margin Classification, Nonlinear SVM Classification, Polynomial Kernel, Adding Similarity F

  WEEK 7:  Apply EM algorithm to cluster a set of data stored in a .CSV file. Use the same data set for clustering using k-Means algorithm. Compare the results of these two algorithms and comment on the quality of clustering.