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Machine Learning Notes

Topics in our Machine Learning Notes PDF

In these “Machine Learning Notes PDF”, you will study the basic concepts and techniques of machine learning so that a student can apply these techniques to a problem at hand.

The topics we will cover will be taken from the following list:

Introduction: Basic definitions, Hypothesis space and inductive bias, Bayes optimal classifier and Bayes error, Occam’s razor, Curse of dimensionality, dimensionality reduction, feature scaling, feature selection methods.

Regression: Linear regression with one variable, linear regression with multiple variables, gradient descent, logistic regression, over-fitting, regularization. performance evaluation metrics, validation methods.

Classification: Decision trees, Naive Bayes classifier, k-nearest neighbor classifier, perceptron, multilayer perceptron, neural networks, back-propagation algorithm, Support Vector Machine (SVM), Kernel functions.

Clustering: Approaches for clustering, distance metrics, K-means clustering, expectation maximization, hierarchical clustering, performance evaluation metrics, validation methods.

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