Practical Recursive Feature Selection
With the Summer 2018 Release Data Transformations were added to BigML. SQL-style queries, feature engineering with the Flatline editor and options to merge and join datasets
Tag: WhizzML
Automating Principal Component Analysis
Today’s post is the fifth one of our series of blog posts about BigML Principal Component Analysis (PCA) unique implementation, the latest
K-means – – Finding Anomalies while Clustering
On November 4th and 5th, BigML joined the Qatar Computing Research Institute (QCRI), part of Hamad Bin Khalifa University, to bring
The Data Transformations Webinar Video is Here: Machine Learning-Ready Data!
The latest BigML release brings new Data Transformation capabilities to our Machine Learning platform, crucially alleviating the data engineering bottleneck.
Automating Data Transformations with WhizzML and the BigML Python Bindings
This is the fifth post of our series of six about BigML’s new release: Data Transformations. This time we are
Automating Fusions with WhizzML and the Python Bindings
This blog post is the fifth in our series of posts about Fusions and focuses on how to automate processes
Finding 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
Automating 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
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
Automating Time Series with WhizzML
Since the beginning of our civilization, humans have worried about the future. In particular, we worry about predicting the future.
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