This short video tutorial shows how to use the BigML Dashboard to predict the customer profile that will buy pink iPhones.
For a gentle introduction to BigML, we recommend the following tutorials that are mostly written or recorded independently by Machine Learning practitioners from around the world.
This monograph aims at providing an introduction to key concepts, algorithms, and theoretical frameworks in Machine Learning, including supervised and unsupervised learning, statistical learning theory, probabilistic graphical models and approximate inference.
In this post, Machine Learning meets fantasy football. The author builds a predictive model to dominate his 2017 fantasy football league with AI and ML.
This post shows how to determine the best predictors of poverty rates across counties in the United States.
Could you predict if a particular wine would be of good or poor quality just by knowing certain climatic conditions during a growing season?
This post narrows down a list of 100+ startups with high probability to become successful and selects the best ones applying Machine Learning.
This post shows how we can use historical stock data to predict venture-backed startup success or failure.
This post explains a use case that uses BigML to find commonalities among terrorists that help us understand who they are.
Predict which of your customers or subscribers are at high risk to churn without a single line of coding.
Using Machine Learning to find patterns among US citizens in order to analyze their votes for the Presidential election.
This video shows how to create a model from a remote CSV file, and use it to make local predictions for new instances using BigML Python Bindings.
With this step by step tutorial you will learn how to improve the results of your ML models by using Machine Learning.
Learn how to do Machine Learning without many data scientists. This is part of the series Data Science Start-Ups in Focus.
Decisions trees have been extensively and actively involved in various application domains with great success.
A walkthrough using BigML's extremely easy interface to model datasets using classification and regression algorithms.
With an automated workflow in Machine Learning you are extracting the outside variables of your data and making it more valuable.
Surprised by the simplicity, functionality, and novelty of the services. The accuracy is higher than Amazon and Google's results.
As a developer, I greatly appreciate the effort in supporting so many programming languages and platforms, making everyone’s work simpler.
Learn about predictive analytics and watch how to solve overfitting with ensembles, evaluate a predictive model, find patterns with clustering, and detect anomalies.
BigML makes Machine Learning more accessible than ever thanks to its well defined workflows, visualizations, and fully featured API.
This post completes a tutorial about data science. This third part explains how to build a predictive model of real estate pricing from the data collected.
This blog post explains how to predict in advance which customers are at risk of leaving, so you can reduce customer retention efforts by directing them solely.
Find out how to create a source, dataset, a predictive model, evaluate it and make predictions on unseen data using the predictive model.
Watch this video to learn how to make predictions with Tableau after exporting your BigML models to Tableau platform.
Discover how to train a predictive model in this timed demo of BigML's amazing speed, using StumbleUpon data from Kaggle.
This application is very compelling for companies who like to work with Machine Learning but don't have yet the budget nor infrastructure.
This blog post explains how to find predictive metrics to figure out if the person applying for a credit card should be approved or not.
We are always interested in improving the understanding of BigML's capabilities, and how those can be utilized to solve real life Machine Learning problems. As such, please contact us if you have other informative or instructive content that we can promote here.