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 segme...

   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 ...

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 Similari...

  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.