In this tutorial, you'll learn how to create contingency tables and how to test and quantify relationships visible in them. Learn the basics of deep learning and neural networks along with some fundamental concepts and terminologies used in deep learning. In this tutorial, you'll learn how to set up Git on your computer in different operating systems. In this tutorial, you will learn about H2O and have a glimpse of its autoML functionality. Learn how to manipulate time series data with pandas and conduct significance testing through simulation using Python, to analyze stock market volatility. In this tutorial, learn what metaclasses are, how to implement them in Python, and how to create custom ones. must read data visualization +2 In this tutorial, you’ll learn how to create publishable and reproducible data science studies on Kyso’s platform, using interactive plotly visualizations. 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.