ATOM is an open-source, easy-to-use machine learning package for Python. ATOM is capable of running experiments quickly and efficiently, enabling the user to go from raw data to generating insights in just a few lines of code.
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ATOM: A Python package for fast exploration of machine learning pipelines
During the exploration phase of a project, a data scientist tries to find the optimal pipeline for his specific use case. This usually involves applying standard data cleaning steps, creating or selecting useful features, trying out different models, etc. Testing multiple pipelines requires many lines of code, and writing it all in the same notebook...
How to test multiple machine learning pipelines with just a few lines of Python
Since it's nearly impossible to know beforehand which transformations will benefit the model's outcome the most, this process usually involves trying out different approaches. For example, if we are dealing with an imbalanced dataset, should we oversample the minority class or undersample the majority...
From raw data to web app deployment with ATOM and Streamlit
In this article we will show you how to create a simple web app, capable of helping a data scientist to quickly perform a basic analysis on the performance of predictive models on a provided dataset. The user will be able to upload his own dataset and tweak the machine learning pipeline in two ways: selecting which data cleaning steps to apply...
Exploration of Deep Learning pipelines made easy
During the exploration phase of a project, a data scientist tries to find the optimal pipeline for his specific use case. In this story, I’ll explain how to use the ATOM package to quickly help you train and evaluate a deep learning model on any given dataset...
Deep Feature Synthesis vs Genetic Feature Generation
Feature engineering is the process of creating new features from the existing ones, in order to capture relationships with the target column that the first set of features didn't have on their own. This process is very important to improve the performance of machine...
From raw text to model prediction in under 50 lines of Python
Natural Language Processing (NLP) is the subfield of machine learning that works with human language data. Working with human text usually involves standard preprocessing steps such as data cleaning and converting the text to vectors of numbers, before being able to make predictions with a machine learning model...