Let's examine the three most common metrics to measure how values are spread out.
Let's examine four measures that you can use to represent such a central value.
I’ll share the topics you’ll need to learn, the best available resources, and the order in which to study them.
You'll learn to do all of that using SciPy. I'll also show you how to plot histograms and density curves for normally distributed data.
Let's write our own function and use it to sketch highly customizable dot plots.
Also, does Scikit-Learn provide a way to handle imbalanced classes?
Let's explore how Precision, Recall, and F1 Score can give a realistic view of a model’s predictive power.
You'll also gain practical skills to generate these metrics using Scikit-Learn.
You'll also learn about Empirical Rule, which dictates how values are spread in specific intervals around the mean.
You'll also learn to plot and analyze partial areas under the curve using Matplotlib, Seaborn, and Numpy.