ML Algorithms Made Simple – Bagging & Boosting

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

Bagging & Boosting Made Simple

Bagging and boosting are two different types of ensemble learners.  Ensemble learning is a method of combining many weak learners together to build a more complex learner.  This is also called ‘meta-learner’ because ensemble learners combine other types of learners to get a final output.  A weak learner is simply any learner that does better than random chance.

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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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Machine Learning Interview Questions – Q9 – Explain your favorite algorithm in less than a minute

Machine learning interview questions is a series I will periodically post on.  The idea was inspired by the post 41 Essential Machine Learning Interview Questions at Springboard.  I will take each question posted there and provide an answer in my own words.  Whether that expands upon their solution or is simply another perspective on how to phrase the solution, I hope you will come away with a better understanding of the topic at hand.

To see other posts in this series visit the Machine Learning Interview Questions category.

Q9 – Explain your favorite algorithm in less than a minute.
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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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Machine Learning Interview Questions – Q8 – Explain the difference between L1 and L2 regularization

Machine learning interview questions is a series I will periodically post on.  The idea was inspired by the post 41 Essential Machine Learning Interview Questions at Springboard.  I will take each question posted there and provide an answer in my own words.  Whether that expands upon their solution or is simply another perspective on how to phrase the solution, I hope you will come away with a better understanding of the topic at hand.

To see other posts in this series visit the Machine Learning Interview Questions category.

Q8- Explain the difference between L1 and L2 regularization

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Machine Learning Interview Questions – Q7 – Why is Naive Bayes naive?

Machine learning interview questions is a series I will periodically post on.  The idea was inspired by the post 41 Essential Machine Learning Interview Questions at Springboard.  I will take each question posted there and provide an answer in my own words.  Whether that expands upon their solution or is simply another perspective on how to phrase the solution, I hope you will come away with a better understanding of the topic at hand.

To see other posts in this series visit the Machine Learning Interview Questions category.

Q7 – Why is Naive Bayes naive?

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Machine Learning Interview Questions – Q6 – What is Baye’s Theorem?

Machine learning interview questions is a series I will periodically post on.  The idea was inspired by the post 41 Essential Machine Learning Interview Questions at Springboard.  I will take each question posted there and provide an answer in my own words.  Whether that expands upon their solution or is simply another perspective on how to phrase the solution, I hope you will come away with a better understanding of the topic at hand.

To see other posts in this series visit the Machine Learning Interview Questions category.

Q6 – What is Bayes’ Theorem?

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