Let's explore Area Under Density Curve. What does it represent? What are some of its practical applications?

You'll also learn to plot and analyze partial areas under the curve using Matplotlib, Seaborn, and Numpy.

Let's explore how Data Distribution enables you to extract general patterns from the data.

You'll also learn to visualize distribution as Histogram and Density Curve using Matplotlib and Seaborn.

Let's explore how you can use Matplotlib to draw pie charts with customized colors and labels. You can even apply styles tailored to each slice.

Along the way, you'll see what's an exploding pie chart and how to draw it. Finally, you'll learn to plot Donut Charts!

Do you want samples that accurately represent the population? Here's how Stratified Sampling can help.

You'll also develop practical skills and learn how to do sampling using Python and Pandas.

Let's explore how to create classification datasets with balanced or imbalanced classes and binary or multiclass labels.

You can even produce datasets that are harder to classify!

Let's learn how to calculate Confusion Matrix and Accuracy using Python libraries.

I'll also show you two different ways to visualize the Confusion Matrix.

Let's look at the basic metrics to estimate a classification model’s predictive performance.

You'll also gain practical skills to generate and visualize these metrics using Scikit-Learn and Seaborn.

How can you perform K-Fold Cross-Validation to evaluate machine learning models?

I'll show you two ways using Python and Scikit-Learn's helper functions - cross_val_score() and cross_validate().

It's time to learn Cross-Validation, the tool serious data scientists use to estimate model performance.

Cross-Validation builds upon Train Test Split and provides a better estimate of a machine learning model's performance.

Sometimes you want to draw boxplots where each column gets its own y-axis.

Here's how you can do it using pandas, Matplotlib, and Seaborn.