“Support Vector Machine” (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. Let's talk in more details.
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All Data Scientists want just one thing and that’s disgusting. It’s “p-value < 0.05”. We are going to talk about how to set up and run statistical hypothesis tests, what’s the difference between null and alternative hypothesis, what is p-value and why we want it to be small. We will cover significance testing of population means and proportions, starting with theory and showing how it can be done in Python and R.
We are going to talk about linear regression, one of the most well known and well understood algorithms in machine learning. We are going to focus on the simple linear regression, which contains only one input variable. But the same logic and analyses will extend to the multi-variable linear regression. Let's talk in more details.
Gradient Boosting is another technique for performing supervised machine learning tasks, like classification and regression. XGBoost stands for Extreme Gradient Boosting; it is a specific implementation of the Gradient Boosting method which uses more accurate approximations to find the best tree model. It employs a number of nifty tricks that make it exceptionally successful, particularly with structured data. Let's talk in more details.
A decision tree is a supervised machine learning model used to predict a target by learning decision rules from features. Classification and Regression Trees is a term to refer to Decision Tree algorithms that can be used for classification or regression predictive modelling problems. Let's talk in more details.