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Top 5 Time Series Analytics
Learn about the top use-cases and techniques for time-series data Time is present in most of the data around us. From retail product sales data to financial stock price, to IoT sensor data, all have a notion of time in it. So mastering time-series analytics is going to make you master of the data science world The top 5 analytics which I will demonstrate here are Seasonality Detection to find (…)
Why Machine Learning Engineers are Replacing Data Scientists
And what you should do about it
Anyscale – Parallelizing Python Code
Python is great for tasks like training machine learning models, performing numerical simulations, and quickly developing proof-of-concept solutions without setting up development tools and installing several dependencies. When performing these tasks, you also want to use your underlying hardware as much as possible for quick results. Parallelizing Python code enables this. However, using the (…)
End-to-end tinyML audio classification with the Raspberry Pi RP2040 — The TensorFlow Blog
https://1.bp.blogspot.com/-r04Uz1ouKDo/YVzmaJWpAxI/AAAAAAAAEnI/xq1XXOcQPAgHym44n2ZJJwAHP1wxR3oaQCLcBGAsYHQ/s0/ezgif.com-gif-maker%2B%25282%2529.gif October 06, 2021 — A guest post by Sandeep Mistry, Arm Introduction Machine learning enables developers and engineers to unlock new capabilities in their applications. Instead of explicitly defining instructions and rules for a computer to execute, (…)
How VSCode has leaped forward as Jupyter Notebook Editor
The improved Notebooks API brings several strengths of VSCode in Python editing to Jupyter Notebooks Working with (Jupyter) Notebooks — After more than half a year of waiting for what was then called “native Notebooks”, the now re-brand e d Notebooks API has finally made it to the standard VSCode version. I have been impatiently waiting for these changes that I missed in the Jupyter browser (…)
Interpreting A/B test results: false positives and statistical significance
Martin Tingley with Wenjing Zheng , Simon Ejdemyr , Stephanie Lane , and Colin McFarland This is the third post in a multi-part series on how Netflix uses A/B tests to inform decisions and continuously innovate on our products. Need to catch up? Have a look at Part 1 (Decision Making at Netflix) and Part 2 (What is an A/B Test?). Subsequent posts will go into more details on experimentation (…)
Removing Background From Images Using AI and Python
Like that super trending app — but powered by Python Image by the author Artificial intelligence has taken the world by storm. The concept has wholly revolutionized almost every other domain as more and more professions integrate artificial intelligence with their respective fields. The following article subsequently explores the use of artificial intelligence for the background removal (…)
End-to-End Introduction to Market Basket Analysis in R
This article was published as a part of the Data Science Blogathon In this article, I’ll go over Market Basket Analysis and how to use it in R. Table of Contents: 1. Market Basket Analysis 2. How is it used? 3. Association Rules 4. Applications 5. The Dataset 6. Math’s in Market Basket Analysis 7. […] The post End-to-End Introduction to Market Basket Analysis in R appeared first on Analytics (…)
How To Make Parallel Async HTTP Requests in Python
Requests with Threads vs. aiohttp with Semaphore By reading this piece, you will learn to make multiple asynchronous requests concurrently in Python. This tutorial covers two different methods: via requests package with Threads (a native thread for each request) via aiohttp client with Semaphore (to limit and pool the number of tasks) Both implementations are inspired by the explanation in the (…)
Python Tricks: Unpacking Iterables
Programming You don’t always need indices Welcome to a series of short posts each with handy Python tricks that can help you become a better Python programmer. In this blog, we will look into how to unpack iterables. You don’t need indices. Say you have a tuple ("a", "b", "c") . If you want to assign the first element to a , the second element to b , and the third (…)
How I Redesigned over 100 ETL into ELT Data Pipelines
Level up your Data Pipelines! Image by Author Everyone: What do Data Engineers do? Me: We build pipelines. Everyone: You mean like a plumber? Something like that, but instead of water flowing through pipes, data flows through our pipelines. Data Scientists build models and Data Analysts communicate data to stakeholders. So, what do we need Data Engineers for? Little do they know, without Data (…)
End-to-End Predictive Analysis on AirBnB Listings Data
This article was published as a part of the Data Science Blogathon Introduction Airbnb is a $75 Billion online marketplace for renting out homes/villas/ private rooms. The website charges a commission (3 to 20 percent, ) for every booking. Even though the prospects are sound, but there are critics who argue that this has driven up […] The post End-to-End Predictive Analysis on AirBnB Listings (…)
Product Manager w zespole AI
Jak wygląda praca Product Managera w zespole AI? Na to i wiele innych pytań odpowie gość tej rozmowy – Ola Możejko. Mieliśmy okazję się poznać podczas organizacji konferencji DataWorkshop Club Conf w 2017 roku, a później widywaliśmy się na wielu innych wydarzeniach. Zawsze można było liczyć na świetną merytorykę jej prezentacji oraz wyjątkową atmosferę, która jej towarzyszyła. Historia Oli jest (…)
Encrypt and Decrypt PDF Files using Python
Introduction Here’s what Adobe has to say about PDFs: PDFs run your world. You know you use PDFs to make your most important work happen. That’s why we invented the Portable Document Format (better known by the abbreviation PDF), to present and exchange documents reliably — independent of software, hardware or operating system. The PDF is now an open standard, maintained by the International (…)
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