How Machine Learning, Classification Models Impact Marketing Ethics - Pierre DeBois @allanalytics


But in relying on algorithms to provide customer convenience, managers must understand classification to protect brands from making unethical societal choices when delivering outcomes to customers.

Introduction to Machine Learning for Developers


The two main types of machine learning algorithms are supervised and unsupervised learning. There are many types of supervised algorithms available, one of the most popular ones is the Naive Bayes model which is often a good starting point for developers since it's fairly easy to understand the underlying probabilistic model and easy to execute. Decision trees are also a predictive model and have two types of trees: regression (which take continuous values) and classification models (which take finite values) and use a divide and conquer strategy that recursively separates the data to generate the tree. Check out the rest of the blog for more resources on natural language processing and machine learning algorithms such as LDA for text classification or increasing the accuracy on a Nudity Detection algorithm and a beginners tutorial on using Scikit-learn to solve FizzBuzz.

Multi-borders classification Machine Learning

The number of possible methods of generalizing binary classification to multi-class classification increases exponentially with the number of class labels. Often, the best method of doing so will be highly problem dependent. Here we present classification software in which the partitioning of multi-class classification problems into binary classification problems is specified using a recursive control language.

Getting started with Azure ML: Two-Class and Multi-Class Classification


In this article we will explain the types of problems you can solve using the Azure ML Two-Class (or Binary) and Multi-Class Classification algorithms and help you build a basic model using them. Hi, I'm Scott Davis, a Data Scientist for Valorem Reply and in this post I'm going to show you step-by-step just how easy (no coding required) it is to make a production-ready machine learning model in Azure ML using a private ML model I've created just for this post. Before we can jump in, you'll need: Before we get going it is good to understand that there are A LOT of things you can do with Azure ML. The purpose of this exercise is to get you started quickly with the tool by building a simple model using basic functionality in Azure ML. In future posts we'll dive deeper into the tool and using Data Science concepts to make it work for your business needs.