| Unit I |
Introduction to Machine Learning |
06 |
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What Is Machine Learning |
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Examples of Machine Learning Applications |
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Learning Associations |
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Classification |
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Regression |
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Unsupervised Learning |
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Reinforcement Learning |
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| Unit II |
Feature Selection |
06 |
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Scikit-learn Dataset |
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Creating training and test sets |
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Managing categorical data |
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Managing missing features |
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Data scaling and normalization |
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Feature selection and filtering |
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Principle Component Analysis (PCA) |
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Non-negative matrix factorization |
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Sparse PCA |
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Kernel PCA |
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| Unit III |
Supervised Learning |
06 |
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Learning a Class from example |
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Linear Regression |
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Logistic Regression |
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Naïve Bayes Classifier |
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Support Vector Machines |
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KNN Algorithm |
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Decision Trees |
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Random Forests |
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Model Evaluation: Overfitting & Underfitting |
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| Unit IV |
Unsupervised Learning |
06 |
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Clustering |
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k-Means Clustering |
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Hierarchical Clustering |
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Agglomerative Clustering |
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Dendrograms |
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Expectation-Maximization Algorithm |
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The Curse of Dimensionality |
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Dimensionality Reduction |
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Factor Analysis |
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| Unit V |
Combining Multiple Learners |
06 |
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Rationale |
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Generating Diverse Learners |
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Voting |
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Bagging |
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Boosting |
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Mixture of Experts Revisited |
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Stacked Generalization |
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Fine-Tuning an Ensemble |
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Cascading |
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| Unit VI |
Advances in Machine Learning |
06 |
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Reinforcement Learning |
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Introduction |
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Elements of Reinforcement Learning |
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Model-Based Learning: Value Iteration |
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Policy Iteration |
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Deep Learning |
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Defining Deep Learning |
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Common Architectural Principles of Deep Networks |
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Building Blocks of Deep Networks |
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