Debiased ML combines bias correction and sample splitting to compute scalar summaries.
The 21-month online programme includes 11 hands-on projects.
This article briefs on the various ways a machine learning model learns from the data with uncertainty and why is probabilistic machine learning the best.
The need for testing and validating data and machine learning models
Logistic regression is a simple classification algorithm used to model the probability of a discrete outcome from a given set of input variables.
Databricks offers a Unified Data Analytics Platform.
DataRobot, powered with the recent open-source algorithms and loaded with on-premise AI services, offers features to build and deploy ML models with ease.
Google is integrating AR into many Google products, from Google Lens to multisearch, scene exploration, and Live and immersive views in Maps.
A deterministic approach is a simple and comprehensible compared to stochastic approach.
The workshop covered extensively the oneAPI AI Analytics toolkit, which contained a core set of tools and libraries for developing high-performance applications on Intel® CPUs, GPUs, and FPGAs.
Overfitting is a basic problem which could be mitigated at various stages of machine learning project.
Continuous-time Markov chain is a type of stochastic process where continuity makes it different from the Markov chain. This process or chain comes into the picture when changes in the state happen according to an exponential random variable.
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