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Based on our record, Babel seems to be a lot more popular than machine-learning in Python. While we know about 134 links to Babel, we've tracked only 7 mentions of machine-learning in Python. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Some of the most popular JavaScript linting tools are ESLint, JSHint, JSLint and JSCS. We're going to be using ESLint. It’s very flexible, easy to use and has the best ES6 support, which will be helpful if we introduce more modern JavaScript (that will be transpiled for older browsers using https://babeljs.io/). All rules for ESLint can be found here: https://eslint.org/docs/rules/. - Source: dev.to / 3 months ago
This simply extends the existing build process that many front-end frameworks have. After Babel's done with its transpilation, it merely executes code to compile your initial screen into static HTML and CSS. This isn't entirely dissimilar from how SSR hydrates your initial screen, but it's done at compile-time, not at request time. - Source: dev.to / 3 months ago
First, we switched the default compiler for new projects from Babel to SWC (Speedy Web Compiler). SWC is dramatically faster than Babel and requires zero configuration. We’ll continue to support Babel in any project currently using it. - Source: dev.to / 4 months ago
Nuxt.js is an open-source JavaScript framework built on Vue.js, Node.js, Vite, and Babel.js used for creating fast, cutting-edge applications. Nuxt.js possesses similar features to Next.js, with the major difference being the web framework it is compatible with. Next.js is a React framework whereas Nuxt.js is a Vue framework. - Source: dev.to / 6 months ago
Disclaimer: If you've already developed Babel or ESLint plugins, this article may not be as beneficial for you, as you're likely already familiar with the majority of the content covered here. - Source: dev.to / 8 months ago
After that you should probably look at some very basic ML tutorials. I just googled it, I have no idea if this is good https://machinelearningmastery.com/machine-learning-in-python-step-by-step/. Source: over 1 year ago
Few different approaches based on search engine 'ml with python': Work though use cases / examples : https://www.databricks.com/resources/ebook/big-book-of-machine-learning-use-cases On-line class(es) / step by step projects: * https://bootcamp-sl.discover.online.purdue.edu/ai-machine-learning-certification-course * https://www.w3schools.com/python/python_ml_getting_started.asp *... - Source: Hacker News / over 1 year ago
MLE: ALL OF THE ABOVE (this is important - pure machine learning skills generally won’t make you hireable unless you’re doing a PhD and/or are a genius) Plus: 1. https://machinelearningmastery.com/machine-learning-in-python-step-by-step/ 2. https://www.coursera.org/learn/machine-learning 3. https://www.3blue1brown.com/topics/neural-networks. Source: about 2 years ago
Have you seen this? https://machinelearningmastery.com/machine-learning-in-python-step-by-step/. Source: over 2 years ago
Machine learning models Fine-tune existing machine learning models for improved accuracy, or create your own custom models. - Source: dev.to / over 2 years ago
jQuery - The Write Less, Do More, JavaScript Library.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
React Native - A framework for building native apps with React
BigML - BigML's goal is to create a machine learning service extremely easy to use and seamless to integrate.
Composer - Composer is a tool for dependency management in PHP.
Google Cloud TPU - Custom-built for machine learning workloads, Cloud TPUs accelerate training and inference at scale.