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Creating your First Time Series Model with BigML’s Dashboard
BigML is bringing Time Series to the Dashboard to help you forecast future values based on your historical data. Time Series is widely used in forecasting stock prices, sales, website traffic,...
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Programming Time Series with BigML’s API
In this blog post, the fourth one of our series of six, we want to provide a brief summary of all the necessary steps to create a Time Series using the BigML API. As stated in our previous post, Time...
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How to create a WhizzML script – Part 1
Series of basic tutorials, to learn WhizzML from scratch. In this first tutorial we will explain where to find WhizzML scripts and how you can use them.
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How to create a WhizzML script – Part 2
In this second post about WhizzML basics, we go deeper into script creation methods. In the previous post, How to create a WhizzML script – Part1, you learned the basic concepts of WhizzML and how to...
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How to create a WhizzML Script – Part 3
In this third post about WhizzML basics, you’ll learn more about tools to create WhizzML scripts. We already covered how to manipulate WhizzML scripts from the Gallery. We also learned how to do the...
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Using a Customized Cost Function to deal with Unbalanced Data
As pointed in this Kdnuggets article, it’s often the case that we only have a few examples of the thing that we want to predict in our data. The use cases are countless: only a small part of our...
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Creating your Deepnets with the BigML Dashboard
The BigML Team has been working hard this summer to bring Deepnets to the platform, which will be available on October 5, 2017. As explained in our introductory post, Deepnets are an optimized...
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Programming Deepnets with the BigML API
So far, we have introduced BigML’s Deepnets, how they are used, and how to create one in the BigML Dashboard. In this post, the fourth in our series of blog posts about Deepnets, we will see how to use...
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Automating Deepnets with WhizzML and The BigML Python Bindings
This blog post, the fifth of our series of six posts about Deepnets, focuses on those users that want to automate their Machine Learning workflows using programming languages. If you follow the BigML...
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How to Create a WhizzML Script – Part 4
As the final installment of our WhizzML blog series (Part 1, Part 2, Part 3), it’s time to learn the BigML Python bindings way to create WhizzML scripts. With this last tool at your disposal, you’ll...
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Predicting TED Talks Popularity
Everyone knows TED talks. TED started in 1984 as a conference series on technology, education, and design. In essence, TED talks aim to democratize knowledge. Nowadays it produces more than 200 talks...
View ArticleCase Study: Reducing False Negatives with Operating Thresholds
So far, as presented in our introductory blog post, we have learned about what operating thresholds are and how they can be useful. Now it is time to deal with an actual dataset and see how altering...
View ArticleUsing Operating Thresholds with the BigML Dashboard
The BigML Team is bringing operating thresholds for your classification model evaluations and predictions. As explained in our previous posts, operating thresholds are a way to improve the performance...
View ArticleProgramming Operating Thresholds with the BigML API
In this post, the fourth of our blog posts for the first release of the year, we will explore how to use operating thresholds from the BigML API. So far we have learned about what operating thresholds...
View ArticleAutomating Operating Thresholds with WhizzML and the BigML Python Bindings
This blog post, the fifth of our series of posts about operating thresholds, focuses on two ways to automate the use of operating thresholds in our predictions (single or batch) and evaluations. The...
View ArticleBigML Organizations: Better Team Productivity on Machine Learning Projects
If you have been paying attention to our blog, you’ll have noticed that lately we have been publishing a series of six posts about operating thresholds, BigML’s newest feature added to the upcoming...
View ArticlePredicting Air Pollution in Madrid
Air pollution is a tremendous problem in big cities, where health issues and traffic restrictions are continuously increasing. The concentration of Nitrogen Dioxide (NO2) is commonly used to determine...
View ArticleFinding your Optimal Model Automatically with Zero Lines of Code
The BigML Team has been working hard to bring OptiML to the platform, which will be available on May 16, 2018. As explained in our previous post, OptiML is an automatic optimization process for model...
View ArticleFinding your Optimal Model Automatically with the BigML API and OptiML
In this post, the fourth of our 6 blog posts focused on optimizing Machine Learning automatically, we will explore how to use OptiML with the BigML API. So far we have covered an introduction to...
View ArticleFinding your Optimal Models Automatically with WhizzML and OptiML
This blog post, the fifth of our series of posts about OptiML, focuses on how to programmatically use this resource with WhizzML, BigML’s Domain Specific Language for Machine Learning workflow...
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