SciKit Learn

Databricks Machine Learning

What’s New in MLflow? Accelerating the Machine Learning Lifecycle

In the last several months, MLflow has introduced significant platform enhancements that simplify machine learning lifecycle management. Expanded autologging capabilities, including a new integration with scikit-learn, have streamlined the instrumentation and experimentation process in MLflow Tracking. Additionally, schema management functionality has been incorporated into MLflow Models, enabling users to seamlessly inspect and control model inference […]

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AI Python

Scikit-Learn with Andreas Müller

Andreas Müller chats with Seth Juarez about his journey, watch for a short overview of scikit-learn and introduce his new dabl project. Stay tuned till the end to find out what he thinks is next for ML.       More Information: DABL Scikit Learn Create a Free account (Azure) Deep Learning vs. Machine Learning  Get Started with […]

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Machine Learning Python

Machine Learning with Scikit-Learn (Part 2)

This second part of the tutorial session from SciPy 2018 provides an introduction to machine learning and scikit-learn “from the ground up” –starting with core concepts of machine learning, some example uses of machine learning, and how to implement them using scikit-learn. Going in detail through the characteristics of several methods, Andreas Mueller and Guillaume […]

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Machine Learning Python

Machine Learning with Scikit-Learn (Part 1)

This tutorial from SciPy 2018 provides an introduction to machine learning and scikit-learn “from the ground up” –starting with core concepts of machine learning, some example uses of machine learning, and how to implement them using scikit-learn. Going in detail through the characteristics of several methods, Andreas Mueller and Guillaume Lemaitre discuss how to pick […]

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AI Machine Learning TensorFlow

Is this the Best Book on Practical Machine Learning?

Hands On Machine Learning with Scikit Learn and Tensorflow published by O’Reilly and written by Aurelien Geron could just be the best practical book on machine learning. In this review, Giles McMullen-Klein explains why and, having also read this book, I have to agree.

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