# Linear Regression and Logistic Regression Explained

In the machine learning world, there has yet to be a model that is 100% accurate, most especially in real-world situations where data can be varied or not necessarily correlated with each other. Consequently, this begs the question: “How is the accuracy of an ML model obtained or measured in the first place in predicting a given output?” Well, it all lies in the ML algorithm a machine uses to estimate or approximate its predictions.

# What is Linear Regression?

In your statistics classes, the so-called “line of best-fit” is precisely the regression line being referred to here. Oftentimes, the process of linear regression involves…

# Gradient Descent for Machine Learning, Explained

Throw back (or forward) to your high school math classes. Remember that one lesson in algebra about the graphs of functions? Well, try visualizing what a parabola looks like, perhaps the equation y = x². Now, I know what you’re thinking: How does this simple graph relate to this article’s title? To how machines learn? Well, it actually points to one of the fundamental concepts of machine learning — optimization.

# What is a Loss Function?

In typical machine learning problems, there is always an input and a desired output. However, the machine doesn’t really know that. Instead, the machine uses some of the input that…

# In-Depth Analysis of Identification Documents Through Vision AI 