ML Algorithms Made Simple – Neural Networks

This is part of my answer to interview question 9 which is to explain your favorite machine learning algorithm in five minutes.

Neural Networks Made Simple

Neural networks are designed to replicate the way human brains learn.  They consist of layers of nodes that are interconnected.  There is first an input layer, followed by any number of hidden layers, and finally an output layer.  The input layer takes in the values of the features in the training set, the output layer produces the final predicted output.

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ML Algorithms Made Simple – Decision Trees

This is part of my answer to interview question 9 which is to explain your favorite machine learning algorithm in five minutes.

Decision Trees Made Simple

A decision tree learns from training data by creating a tree structure.  This tree structure is made up of nodes, edges, and leaves.  

  • Each node asks a question about a particular attribute in the data.  
  • Each edge represents the different values that particular attribute holds and the path to the next node or leaf.  
  • Every leaf is the end of that path in the decision tree and represents the final output from the decision tree model along that path.

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ML Algorithms – Understanding Decision Trees – Part 2 of 2

In part 1 of this series, you learned what the components of a decision tree are, what the advantages of using a decision tree are, how to prevent overfitting, and about ensemble learning.  In this post, you will learn how the decision tree algorithm is implemented and what it means to pick the “best” attribute.

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ML Algorithms – Understanding Decision Trees – Part 1 of 2

Decision trees are one of the most popular machine learning algorithms in use today.  In this post you will learn what the components of a decision tree are, ways to prevent overfitting decision trees, and when the best time to use a decision trees is.  In part 2 you will learn how decision trees are implemented.

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