Data Science Tutorials

Learn how to build and evaluate a Naive Bayes Classifier using Python's Scikit-learn package.

In this tutorial, you'll learn the basic concepts and terminologies of reinforcement learning. At the end of the tutorial, we'll discuss the epsilon-greedy algorithm for applying reinforcement learning based solutions.

In this tutorial, you are going to learn how to work with Zip Files in Python using the zipfile module. zipfile is a Python built-in module.

Learn about automated machine learning and how it can be done with auto-keras.

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.

In this tutorial, you will learn about different factors that are taken into consideration before choosing your Analytics Platform both at the organization and individual level.

In this tutorial, you'll learn the importance of IDEs, how to set-up Atom, and download packages.

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.

Understand the ensemble approach, working of the AdaBoost algorithm and learn AdaBoost model building in Python.

Find out how to import data into R, including CSV, JSON, Excel, HTML, databases, SAS, SPSS, Matlab, and other files using the popular R packages.

A tutorial on loops in R that looks at the constructs available in R for looping. Discover alternatives using R's vectorization feature.

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.