Data Science, Machine Learning, AI & IOT

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Sponsored Post Yachay.ai (https://www.yachay.ai/) has launched a machine learning hackathon focused on advancing the development of text-to-location models. Participants have the chance to win a prize of $500! If you're up for the challenge, here are instructions to participate: https://github.com/Yachay-AI/confident-predictions-challenge. The submission deadline is November 25, 2023. Yachay.ai team has built and open-sourced a architecture for geotagging models that take textual inputs and produce coordinates as outputs. Codebase here: https://github.com/Yachay-AI/byt5-geotagging. Currently, they are actively seeking ways to enhance validation metrics. If you are looking for ideas on leveraging geotagging models in your projects, join the discussion on potential use cases: https://discord.com/invite/PaqpppYTxs Yachay.ai plans to release a downloadable model for Spanish texts and provide more free text/coordinates datasets in the near future. Stay tuned for updates!

Python in Excel – making it possible to integrate Python and Excel analytics within the same Excel grid for uninterrupted workflow. @kdnuggets @datasciencechats #python #excel Blog: https://techcommunity.microsoft.com/t5/microsoft-365-blog/introducing-python-in-excel-the-best-of-both-worlds-for-data/ba-p/3905482 Video: https://www.youtube.com/watch?v=H4XbvL8Mglc

Virtual Medicine has created the most advanced, multi-award winning medical anatomy platform crossing Virtual Reality (VR), Mixed Reality (MR), and Augmented Reality (AR) for medical education. @kdnuggets @datasciencechats #VR #AR https://www.instagram.com/reel/CwUbqbZqTuI/?igshid=MzRlODBiNWFlZA== Join our instagram channel for more: https://www.instagram.com/kdnuggets/

Stanford online has released the course videos on "Natural Language Understanding" by Professor Christopher Potts. The course covers topics including ⦿ Intro & evolution of NLU ⦿ Contextual word representations ⦿ Information retrieval ⦿ In-context learning ⦿ Behavioural evolution of NLU models ⦿ Analysis methods for NLU ⦿ NLP methods and metrics ⦿ Presenting your research ⦿ Language models and how to build them @datasciencechats @kdnuggets #stanford #nlp Credit: Linkedin Sudalai Rajkumar https://www.youtube.com/playlist?list=PLoROMvodv4rOwvldxftJTmoR3kRcWkJBp

Language translation + speech recognition + speech synthesis in a single model: speech-to-speech, text-to-text, speech-to-text and text-to-speech. Works for 100 languages. Code available under CC-BY-NC license. From Meta - Fundamental AI Research. #meta #speech #language #text Blog post: https://bit.ly/45z0e6s Details ➡️ https://bit.ly/47In6m0 Demo ➡️ https://bit.ly/45jjYLz Code ➡️ https://bit.ly/3KRne8W @kdnuggets @datasciencechats

MediSearch gives direct answers to medical questions based on trust worthy sources using llms https://medisearch.io #llm #machinelearning @kdnuggets @datasciencechats

Microsoft announcements #generativeai @kdnuggets @datasciencechats https://www.instagram.com/reel/Cu4AbqAM444/?igshid=Y2I2MzMwZWM3ZA==

https://www.classcentral.com/report/openai-chatgpt-course/ #chatgpt For more @coursenuggets

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https://ai.facebook.com/blog/segment-anything-foundation-model-image-segmentation/ #ai #imagesegmentation @kdnuggets @datasciencechats

First thing if you are a beginner and have decided you want to learn Data Science then know that it is a journey (6 months - 1 year at least) and not a 1 month thing where u do some courses and you are a data scientist. There are different fields in Data Science that you have to first get familiar and strong in basics as well as do hands-on to get the abilities that are required to function in a full time job opportunity. Then further delve into advanced implementations. There are plenty of roadmaps and online content both paid and free that you can follow. In a nutshell. A few essential things that will be necessary and in no particular order that will at least get your data science journey started are below: Basic Statistics, Linear Algebra, calculus, probability Programming language (R or Python) - Preferably Python if you rather want to later on move into a developer role instead of sticking to data science. Machine Learning - All of the above will be used here to implement machine learning concepts. Data Visualisation - again it could be simple excel or via r/python libraries or tools like Tableau,PowerBI etc. This can be overwhelming but again its just an indication of what lies ahead. So most important thing is to just START instead of just contemplating the best way to go about this. Since lot of things can be learnt independently as well in no particular order. You can use the below Sources to prepare your own roadmap: @coursenuggets - some free courses from here @kdnuggets - check & search in this channel with #beginner Python - https://bit.ly/38vaKjc Kaggle - https://www.kaggle.com/learn Udemy - https://bit.ly/3wmpvjH Coursera - https://bit.ly/3o1R7ab

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45M datasets from more than 13k websites. Dataset Search a dedicated search engine for datasets @kdnuggets @datasciencechats #datasets https://ai.googleblog.com/2023/02/datasets-at-your-fingertips-in-google.html

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Skills needed for Data Science #datascience #roadmap @kdnuggets @datasciencechats

Data Analyst skills #dataanalyst @kdnuggets @datasciencechats

Data Analyst Roadmap #dataanalyst @kdnuggets @datasciencechats

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Stanford course CS330 Deep Multi-task and Meta Learning lectures are now available for free on YouTube Material http://cs330.stanford.edu/fall2021/ @coursenuggets @kdnuggets @datasciencechats #stanford #deeplearning #machinelearning #artificialintelligence https://www.youtube.com/playlist?list=PLoROMvodv4rMIJ-TvblAIkw28Wxi27B36