Machine-Learning-Model-Tutorials

Here is a collection of comprehensive tutorials I wrote on some of the most important machine learning models. They include conceptual information, a project build, data source, and source code.

1. The Simplest Guide to Support Vector Machines.

Support Vector Machines(SVM) are one the most frequently used machine learning models, and definitely essential for any ML developer's toolkit. The goal of this article isn’t just to simply teach you what SVM’s are but also how to build one with python.

2. The Simplest Guide to Naive Bayes Classifiers.

The Naive Bayes algorithm is one of the most commonly used machine learning algorithms out there. The goal of this article is to not only teach you how Naive Bayes works but also how to build one with Python.

3. The Simplest Guide to Neural Networks.

An article post that informs the reader of general information regarding Neural Networks as well as how to build one with Python. The problem set takes in practice hours and working hours to predict ones performance in a football game, but the coding is highly flexible so it can be implemented in many different ways.

4. The Simplest Guide to the Random Forest Algorithm.

In my previous article on Decision Trees, I covered everything about Decision Trees and how to build one with Python. The Random Forest Algorithm is a successor to Decision Trees as it is composed of many trees. In this article, I explain not only how a Random Forest works but also how to build one.

5. The Simplest Guide to the Descion Trees.

The Decision Tree Algorithm is one of the best machine learning models that exist, and fortunately, it is also very easy to build in python. The goal of this article is to not only understand how Decision Trees work but also how to create one of your own.

6. The Simplest Guide to the Logistic Regression.

Logistic Regression is one of the best and easiest machine learning models that exist. In this article, we will cover not only how a Logistic Regression(LR) works but also code one with python using sckit-learn.

7. The Simplest Guide to the Linear Regression

Linear algebra is extremely important in the topic of computer science and especially machine learning. Linear regression is a simple supervised machine learning model directly built off linear algebra. The goal of this article is not to give you a math lesson but instead walk you through the algebra that makes up a regression model and then how to build one in python.

8. The Simplest Guide to K-Means Clustering.

K-Means Clustering is one of the best segmentation models in Machine Learning. It may be the best-unsupervised method there is. This article is a guide through not only what K-Means Clustering is but also how to build one with python.