Link : http://t.me/wethemadians

​​CREME – python library for online ML

All the tools in the library can be updated with a single observation at a time, and can therefore be used to learn from streaming data.

The model learns from one observation at a time, and can therefore be updated on the fly. This allows to learn from massive datasets that don't fit in main memory. Online machine learning also integrates nicely in cases where new data is constantly arriving. It shines in many use cases, such as time series forecasting, spam filtering, recommender systems, CTR prediction, and IoT applications. If you're bored with retraining models and want to instead build dynamic models, then online machine learning might be what you're looking for.

Here are some benefits of using creme (and online machine learning in general):
• incremental – models can update themselves in real-time
• adaptive – models can adapt to concept drift
• production-ready – working with data streams makes it simple to replicate production scenarios during model development
• efficient – models don't have to be retrained and require little compute power, which lowers their carbon footprint


api reference: https://creme-ml.github.io/content/api.html
github: https://github.com/creme-ml/creme

Last time the seats got booked very soon and many were not able to participate.

And Looking at what amazing things participants of Batch 1 have build and after having best reviews from them we are coming up with the Batch 2 for AI Chatbot Development workshop just for you.

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​​Practitioner’s Guide to Statistical Tests
CoreML team at VK

If you want to learn how to choose the right statistical test from the many available and run it on your own data you can find the answer at this article.

The two most essential things in A/B tests are the design of the experiments and accurate analysis of the experiments’ results. In this article, the authors stuck to the most common design and compare various statistical analysis procedures, from the very standard t-test and Mann-Whitney test to state-of-the-art approaches like the reweighted bootstrap.


article: https://medium.com/@vktech/practitioners-guide-to-statistical-tests-ed2d580ef04f
github: https://github.com/marnikitta/stattests

#statistic #ab #tests #vktech

Do read this article! It will add enough value to your statistical knowledge. 💯

Time for understanding ML and DL in Automobile Industry from a Data scientist at Mercedes-Benz.

Check out this link :

https://youtu.be/mpEKT8D6ojk

Also do get your free Masterbook to read from this Masterclass on

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An Introduction to the Powerful Bayes' Theorem for Data Science Professionals
https://www.analyticsvidhya.com/blog/2019/06/introduction-powerful-bayes-theorem-data-science/

Have you ever wondered Why Naive Bayes is called Naive?

Check this recent post out :

https://www.instagram.com/p/CDBaAdBAQWH/?igshid=18e79sd43y4p4

Like, share and comment if you found it valuable.

Machine Learning taught right ❤️

And here a Massive Giveaway Begins 🔥🔥🔥

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Planning to build Web Development Career?

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MAD is starting its 4 Days Web-Dev Bootcamp for free today. If you haven't registered yet must check this link :

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What's inside?

- Frontend Mania (Client-side)
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