Machine Learning


Este congreso, motivado por la creciente sensibilidad de las compañías en materia de Gobierno, Riesgo y Cumplimiento, se enfoca en generar una visión global de los procesos, gestión de riesgos, fraude, control interno y cumplimiento normativo y legislativo, sin dejar de lado la metodología y ejecución de revisiones y auditorías de los mismos

The need for a system view to regulate artificial intelligence/machine learning-based software as medical device

FDA need to widen their scope from evaluating medical AI/ML-based products to assessing systems. This shift in perspective—from a product view to a system view—is central to maximizing the safety and efficacy of AI/ML in health care, but it also poses significant challenges for agencies like the FDA who are used to regulating products, not systems. We offer several suggestions for regulators to make this challenging but important transition

Made With ML

Main website: Summary Made With ML is a platform for the ML community to discover, build and share projects. Our goal is to create a learning space where all of the best ML content is tagged, organized and curated. And as you use the platform and learn, we guide you through building projects and …

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Deep Learning or Machine Learning? (MathWorks)

“In this ebook, we discuss some of the key differences between deep learning and traditional machine learning approaches. We look at three factors that might influence your decision and then step through an example that combines the two approaches”. (MathWorks).

TPOT is a Python Automated Machine Learning tool

Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning (AutoML) tool that optimizes machine learning pipelines using genetic programming.

AutoML: a introduction tutorial about H2O Driverless AI

“H2O has been the driver for building models at scale. We are talking about billions of claims. You can’t do this with standard off the shelf open source techniques”. (

Machine learning in Python: the top programming language

Phyton’s most notable points are:
-Is a great library ecosystem (Scikit-learn, Pandas, Matplotlib, NLTK, Scikit-image, PyBrain, Caffe, StatsModels, TensorFlow, Keras, etc).
-Growing popularity.
-Has a low entry barrier, has flexibility, is a platform independence, has readability, good visualization options, good community support and growing popularity.