## Insightful Tutorials for Inquisitive Developers Here's our latest articles on Machine Learning, React, REST APIs, Databases, and more..

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.

# Measures of Central Tendency: Mean, Median, Weighted Mean, and Mode

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.

# Master Machine Learning and Deep Learning in 2023: A Self-Study Guide

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

# Normal Distribution: A Practical Guide Using Python and SciPy

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.

# Draw Dot Plot Using Python and Matplotlib

Matplotlib doesn't support dot plots natively. So how can you draw them?

Let's write our own function and use it to sketch highly customizable dot plots.

# Precision, Recall, and F1 Score: A Practical Guide Using Scikit-Learn

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?

# Precision, Recall, and F1 Score: When Accuracy Betrays You

Accuracy can be a misleading metric for certain types of classification problems.

Let's explore how Precision, Recall, and F1 Score can give a realistic view of a model’s predictive power.

# 3 Regression Metrics You Must Know: MAE, MSE, and RMSE

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.

# Normal Distribution and the Empirical Rule

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.

# Area Under Density Curve: How to Visualize and Calculate Using Python

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.