Learn what ML models are, the main types with real examples, and a practical framework for choosing the right machine learning model for your business.

Print article Share Artificial intelligence (AI) is developing at a rapid pace and is increasingly being used by scientists to better understand climate change. AI is perhaps best-known for its...

Print article Share Artificial intelligence (AI) is developing at a rapid pace and is increasingly being used by scientists to better understand climate change. AI is perhaps best-known for its...

Print article Share Artificial intelligence (AI) is developing at a rapid pace and is increasingly being used by scientists to better understand climate change. AI is perhaps best-known for its...

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Learn about Tabular Foundation Models such as TabPFN, how they differ from traditional ML models, as well as the new capabilities they unlock for AI agents - pyladiesams/tabular-foundation-models-t...

During this workshop, you'll get an intro into the new era of tabular foundation models (TFMs), how they differ from traditional ML models, as well as the new capabilities

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Explore how machine learning is revolutionizing the fintech industry. Discover key advantages, real-world applications, and the emerging trends shapin

15 Python libraries that will make your (data science) life easier I sort the libraries based on their main functionality. Machine learning, modelling and evaluation  👉 Scikit-learn : https://buf...

Provides a collection of maximum likelihood estimators with a consistent S3 interface. Supported models include Gaussian (linear and log-normal), logit, probit, Poisson, negative binomial (NB1 and NB2), gamma, and beta regression. A distinctive feature is flexible modeling of the scale parameter (variance, dispersion, precision, or shape) alongside the location/mean parameters. The package offers unified predict() methods, multiple variance-covariance estimators (observed information, outer product of gradients, robust/Huber-White, cluster-robust, bootstrap, jackknife), and a full suite of hypothesis tests (Wald, likelihood ratio, information matrix, Vuong, overdispersion, and goodness-of-fit). It is fully compatible with 'marginaleffects' for post-estimation analysis. Methods implemented include Cameron and Trivedi (1990) &lt;<a href="https://doi.org/10.1016%2F0304-4076%2890%2990014-K" target="_top">doi:10.1016/0304-4076(90)90014-K</a>&gt;, for Poisson overdispersion testing, Manjon and Martinez (2014) &lt;<a href="https://doi.org/10.1177%2F1536867X1401400406" target="_top">doi:10.1177/1536867X1401400406</a>&gt;, for goodness-of-fit testing of count data models, Vuong (1989) &lt;<a href="https://doi.org/10.2307%2F1912557" target="_top">doi:10.2307/1912557</a>&gt;, for non-nested likelihood ratio testing, and White (1982) &lt;<a href="https://doi.org/10.2307%2F1912526" target="_top">doi:10.2307/1912526</a>&gt;, for information matrix tests.

Combining machine learning with causal inference improves accuracy and provides deeper insights into data, enhancing predictive power and decision-making.

ML models are being targeted, attacked, and exploited by hackers who want to hack into these systems for several reasons like stealing