Machine Learning Notes PDF
Date: 31st Jan 2023
In these “Machine Learning Notes PDF”, we will study the basic concepts and techniques of machine learning so that a student can apply these techniques to a problem at hand.
We have provided multiple complete Machine Learning PDF Notes for any university student of BCA, MCA, B.Sc, B.Tech CSE, M.Tech branch to enhance more knowledge about the subject and to score better marks in the exam. Students can easily make use of all these Machine Learning PDF Notes by downloading them.
Topics in our Machine Learning Handwritten Notes PDF
The topics we will cover in these Machine Learning Handwritten Notes PDF 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, overfitting, 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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Machine Learning Books
We have listed the best Machine Learning Books that can help in your Machine Learning exam preparation:
Machine Learning PDF Notes FAQs
Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed.
ANN learning is robust to errors in the training data and has been successfully applied for learning real-valued, discrete-valued, and vector-valued functions containing problems such as interpreting visual scenes, speech recognition, and learning robot control strategies.
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