Machine Learning Tutorial

Go through the ten most important updates introduced in the newly released TensorFlow 2.0, and learn how to implement some of them.

An easy-to-follow scikit-learn tutorial that will help you get started with Python machine learning.

In this tutorial to deep learning in R with RStudio's keras package, you'll learn how to build a Multi-Layer Perceptron (MLP).

Learn how to build a neural network and how to train, evaluate and optimize it with TensorFlow

In this tutorial, you'll learn how to use predictive analytics to classify song genres.

In this tutorial, you'll learn about the basics of Hypothesis Testing and its relevance in Machine Learning.

In this tutorial, learn Decision Tree Classification, attribute selection measures, and how to build and optimize Decision Tree Classifier using Python Scikit-learn package.

In this tutorial, you will get a glimpse of encoding techniques along with some advanced references that will help you tackle categorical variables.

In this tutorial, you will learn about H2O and have a glimpse of its autoML functionality.

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.

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.