Machine Learning Tutorial

In this tutorial, you will be introduced to the world of Machine Learning (ML) with Python. To understand ML practically, you will be using a well-known machine learning algorithm called K-Nearest Neighbor (KNN) with Python.

This small tutorial is meant to introduce you to the basics of machine learning in R: it will show you how to use R to work with KNN.

In this tutorial, you'll get an overview of Artificial Intelligence (AI) and take a closer look in what makes Machine Learning (ML) and Deep Learning different.

Learn to how to create a simple API from a machine learning model in Python using Flask.

This tutorial covers stemming and lemmatization from a practical standpoint using the Python Natural Language ToolKit (NLTK) package.

In this tutorial, you will gain knowledge on filling null values, preprocessing data, reducing dimensionality using PCA, and split data using K-Fold.

Learn the techniques to deal with an imbalanced dataset.

Learn about the basic statistics required for Data Science and Machine Learning in Python.

In this tutorial, you will learn how to use a very unique library in python, TPOT. The reason why this library is unique is that it automates the entire Machine Learning pipeline and provides you with the best performing machine learning model.

There is an optimal number of hidden layers and neurons for an artificial neural network (ANN). This tutorial discusses a simple approach for determining the optimal numbers for layers and neurons for ANN's.

In this tutorial, you'll learn what ensemble is and how it improves the performance of a machine learning model.

Learn linear algebra through code and visualization.