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Datanami RSS Newsfeeds -- ScienceDaily Essential Cheat Sheets for Machine Learning and Deep Learning Engineers | by Kailash Ahirwar | Startups & Venture Capital 12. Dask I am a Co-Founder of MateLabs, where we have built Mateverse, an ML Platform which enables everyone to easily build and train Machine Learning Models, without writing a single line of code. Note: Recently, I published a book on GANs titled “Generative Adversarial Networks Projects”, in which I covered most of the widely popular GAN architectures and their implementations. Paper.li
Intelligence artificielle L'Usine Digitale respects your choices! We and our partners process data for the following purposesAnonymous audience statistics, Audience Measurement, Ensure security, prevent fraud, and debug, Functional cookies, Instant Messaging Chat, Personalised advertising and content, advertising and content measurement, audience research and services development , Precise geolocation data, and identification through device scanning, Store and/or access information on a device Wonder How To Machine Learning - KDnuggets 5 Free Courses to Understand Machine Learning Algorithms - Oct 24, 2024. To help you navigate this complex subject, we’ve compiled five free online courses that will give you a solid foundation in machine learning algorithms. Machine Learning 10 GitHub Repositories for Advanced Machine Learning Projects - Oct 16, 2024.Where can you find projects dealing with advanced ML topics? GitHub is a perfect source with its many repositories. I’ve selected ten to talk about in this article. Machine Learning Step-by-Step Guide to Deploying ML Models with Docker - Oct 8, 2024.Tired of fixing the same deployment issues?
Machine learning in Python with scikit-learn Ce que vous allez apprendre À la fin de ce cours, vous serez capable de : Grasp the fundamental concepts of machine learningBuild a predictive modeling pipeline with scikit-learnDevelop intuitions behind machine learning models from linear models to gradient-boosted decision treesEvaluate the statistical performance of your models Please note that this course is available in english only. Description Predictive modeling brings value to a vast variety of data, in business intelligence, health, industrial processes… It is a pillar of modern data science. This course is an in-depth introduction to predictive modeling with scikit-learn. The course covers the software tools to build and evaluate predictive pipelines, as well as the related concepts and statistical intuitions. The training will be essentially practical, focusing on examples of applications with code executed by the participants. Prérequis The course aims to be accessible without a strong technical background.
towardsdatascience There are thousands of different tutorials out there that tell you how to explore your data. Most of them, however, focus on continuous data. Therefore, I won't waste any of your time (or mine) and I will stick to highlighting methods and tools that are specifically useful in survey data. Describe (Numpy version) There are a few inbuilt functions that can help you understand your a lot more, really fast. Describe is a really common tool that is used often by data scientists but this only accounts for the numeric and continuous variables. Some survey software's will output the questions already in one hot format. Groupby Crosstabs and Heatmaps Looking at subgroups of the data can be extremely important, especially in survey data. Groupby I won't dig too deep into how groupby works, but if you want to know more there’s a detailed explanation here. So with our data, we could produce something like this. Crosstabs We can do this with both raw counts or percentages as the code shows. Heat-maps
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