Engineering Natural Language Processing for Automated Feature Engineering How to apply the nlp-primitives library using Featuretools.
Engineering Automatic Dataset Normalization for Feature Engineering in Python A normalized, relational dataset makes it easier to perform feature engineering. Unfortunately, raw data for machine learning is often stored as a single table, which makes the normalization process tedious and time-consuming.
Leadership Featuretools Year in Review Cheers to a fun year helping data scientists and developers build better machine learning models with automated feature engineering. Here's 2018 by the numbers.
Engineering Modeling: Teaching a Machine Learning Algorithm to Deliver Business Value How to train, tune, and validate a machine learning model
Engineering Feature Engineering: What Powers Machine Learning How to Extract Features from Raw Data for Machine Learning
Engineering Prediction Engineering: How to Set Up Your Machine Learning Problem An explanation and implementation of the first step in solving problems with machine learning.
Engineering How to Create Value with Machine Learning A General-Purpose Framework for Defining and Solving Meaningful Problems in 3 Steps.
Engineering Featuretools: One year of automating feature engineering Happy birthday, Featuretools! One year ago, we open sourced Featuretools, making it available to the entire world.
Engineering Featuretools on Spark Distributed feature engineering in Featuretools with SparkApache Spark is one of the most popular technologies on the big data landscape. As a framework for distributed computing, it allows users to
Engineering Scaling Featuretools with Dask How to scale automated feature engineering using parallel processingWhen a computation is prohibitively slow, the most important question to ask is: “What is the bottleneck?” Once you know the answer,
Engineering Why Automated Feature Engineering Will Change the Way You Do Machine Learning Automated feature engineering will save you time, build better predictive models, create meaningful features, and prevent data leakage There are few certainties in data science — libraries, tools, and algorithms constantly
Engineering What Is Machine Learning 2.0? As the demand from businesses to leverage machine learning continues growing at an exponential rate, the current time-intensive process that heavily relies on highly-skilled ML experts won’t suffice.
Engineering Deep Feature Synthesis: How Automated Feature Engineering Works The artificial intelligence market is fueled by the potential to use data to change the world. While many organizations have already successfully adapted to this paradigm, applying machine learning to
Engineering Feature Engineering vs Feature Selection All machine learning workflows depend on feature engineering and feature selection. However, they are often erroneously equated by the data science and machine learning communities. Although they share some overlap,
Leadership Feature Engineering: Secret to data science success Feature engineering is challenging because it depends on leveraging human intuition to interpret implicit signals in datasets that machine learning algorithms use. Consequently, feature engineering is often the determining factor in whether a data science project is successful or not.
Engineering Bringing data science automation to the domain expert’s playground In July, we visited Carnegie Mellon University to participate in the 17th annual Simon Initiative’s LearnLab Summer School on Educational Data Mining. During the program, we gave a talk
Engineering Open Sourcing Featuretools I created Deep Feature Synthesis two years ago while I was a student at MIT. My intention from the very beginning was to one day share that technology with the
