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Artificial Intelligence - Data Science - ML

Artificial Intelligence - Data Science - ML

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Channel created to share articles, Views, Analysis , Repositories Link, in the field of Data Science, Machine Learning and Artificial Intelligence

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Researchers reviewed over 19,000 work tasks, especially those involving language, to figure out their need for human input an
Researchers reviewed over 19,000 work tasks, especially those involving language, to figure out their need for human input and predict how AI might change jobs. IT & Finance Are Likely to See the Most AI Changes. Here are the results.

*Startups across the world, remotely hiring freshers in data/AI roles right now!*🦾 - Data Engineer - https://www.workatastartup.com/jobs/48680 - ⁠Machine Learning Developer - https://www.workatastartup.com/jobs/46959 - ⁠Machine Learning Engineer - https://www.workatastartup.com/jobs/42055 - ⁠Generative AI Engineer - https://www.linkedin.com/jobs/view/3821140465 - ⁠Data Scientist - https://www.linkedin.com/jobs/view/3820785796 - ⁠Content Writer - Artificial Intelligence - https://www.linkedin.com/jobs/view/3814263081 - ⁠Intern - Generative AI - https://www.linkedin.com/jobs/view/3798641318 - ⁠AI and Blockchain Research Analyst - https://www.linkedin.com/jobs/view/3815121181 - ⁠AI Tutor - https://www.linkedin.com/jobs/view/3791151784

Explore Today's Highlights: Dive into our curated selection of top articles, specially picked for you. 🔸10 GPTs for Your Daily Use https://analyticsindiamag.com/10-gpts-for-your-daily-use/ 🔸GitHub Positions AI as Pivotal in Software Development Journey https://analyticsindiamag.com/github-positions-ai-as-pivotal-in-software-development-journey/ 🔸When, and When Not to RAG https://analyticsindiamag.com/when-and-when-not-to-rag/

9 FREE #GenerativeAI Courses from Google👇 1. Introduction to Generative #AI: lnkd.in/gWD78CW9 2. Introduction to #LLMs Large Language Models: lnkd.in/g4wdBKHu 3. Introduction to Responsible AI: lnkd.in/gXCNePhA 4. Introduction to Image Generation: lnkd.in/gjjEGxcV 5. Encoder-Decoder Architecture: lnkd.in/gesTXkgF 6. Attention Mechanism: lnkd.in/g2evVK5Q 7. Transformer Models and BERT Model: lnkd.in/gveBJYq8