3. Machine Learning for Beginners This covers and helps you learn: – Various ML Techniques
– Building your first ML Project right from data collection to create a web app for a trained model
– NLP Techniques
– Times Series
– Reinforcement Learning https://
microsoft.github.io/ML-For-Beginne
rs/#/
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TOOLS
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Machine Learning for Beginners: Complete Guide to ML Techniques and Projects
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Hugging Face Acquires Argilla for ML Data Management
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We're excited to welcome @argilla_io to the Hugging Face team!

Time to democratise good Machine Learning, one dataset at a time! -
MathWorks MATLAB Usability Night: Shape Software Future Together
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Join us at MathWorks HQ in Natick, MA, on June 25, 2024, for an exclusive @MATLAB Usability Night! Dive into new features, provide feedback, enjoy dinner, and network with the team. Your insights help shape MATLAB's future. RSVP now https://
spr.ly/60185BUUj -
LinkedIn Deploys New Generative AI Features for Premium
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[#Article]
@LinkedIn deploys new generative AI features for its premium subscribers https://actuia.com/actualite/linkedin-deploie-de-nouvelles-fonctionnalites-dia-generative-pour-ses-abonnes-premium/
… #AI #ArtificialIntelligence -

AutoTrain Adds Image Scoring and Regression Task Support
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New Task ALERT Image scoring/regression has now been added to AutoTrain Its probably safe to say that AutoTrain is the only no-code open-source solution which provides so many tasks!
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Three Levels of LLM Evaluation Systems for AI Products
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An insightful blog by @HamelHusain on "Your AI Product Needs Evals" – a must-read for anyone looking to construct robust LLM evaluation systems for their applications. Here are the three levels of creating LLM evaluation systems: ✨ Level 1 – Unit Tests: ▪ Write scoped tests like assertion, regex confirmation, etc. ▪ Create test cases (manually or using LLMs). ▪ Run and track tests regularly whenever there is a change in the system. ✨ Level 2 – Human & Model Evaluation: ▪ Log the traces of the LLM and the system. ▪ Manually review traces to check for failures and improvements. ▪ Utilize LLMs as evaluators. ✨ Level 3 – A/B Testing: ▪ Conduct A/B testing of the LLM system against the current baseline system. Dive into the blog for a deeper understanding – hamel.dev/blog/posts/evals/
→ View original post on X — @sudalairajkumar, 2024-06-14 06:44 UTC
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Awesome LLM Apps Demo with RAG GitHub Repository
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Find all the awesome LLM Apps demo with RAG in the following Github Repo. P.S: Don't forget to star the repo to show your support