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What is Machine Learning?
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Machine Learning Paradigms
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Linear Regression
a. Linear Two class classification
b. Linear multi class classification
c. Linear unsupervised learning
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Kernel Methods
a. Fixed shape and universal approximation
b. Kernel trick
c. Optimization
d. Cross validation
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Neural Network
a. Fully connected neural network
b. Activation function
c. Optimization
d. Early stopping
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Tree based learner
a. Regression Trees
b. Classification trees
c. Gradient boosting
d. Random forest
e. Cross validation
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Machine Learning Refined: Foundations, Algorithms, and Applications: Jeremy Watt, Reza Borhani, Aggelos K. Katsaggelos
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Introduction to Machine Learning Etienne Bernard