Insightful Tutorials for Inquisitive Developers

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How do you quantify **the distance between a typical value and the center** of a dataset?

Let's examine the three most common metrics to measure how values are spread out.

How can you choose **one value that summarizes** and captures the essence of **a given data set**?

Let's examine four measures that you can use to represent such a central value.

Are you ready to embark on your machine learning journey? Let me be your guide.

I’ll share the **topics you’ll need to learn**, the **best available resources**, and the order in which to study them.

How can you generate samples from a **Normal Distribution**? How do you calculate **probabilities** and **percentiles**?

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.

How can we use **Scikit-Learn** to measure **Precision**, **Recall**, and **F1 Score** for classification models?

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.

This post demystifies the most common metrics used to evaluate regression models.

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

This post introduces you to **Normal Distribution** and some of its distinctive features.

You'll also learn about **Empirical Rule**, which dictates how values are spread in specific intervals around the mean.

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**.