A new tool makes it easier for database users to perform complicated statistical analyses of tabular data without the need to know what is going on behind the scenes.
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Data Science Researcher | I Solve Business Problems and Research Questions using Statistics | Actively Seeking Full-Time Roles Starting from Aug 2024
Can We Train a ML Model with Data That Has Missing Values? Absolutely, yes! Although not directly, it is possible by using a statistical method called Full Information Maximum Likelihood (FIML). FIML uses maximum likelihood to estimate the parameters of the model by utilizing the existing data and appropriately handling the missing data. There are two major benefits to this approach: it reduces bias, which can occur due to data imputation, and it prevents the loss of information from other features due to one feature having a missing value. For more information, check out my blog on FIML on Medium:
How to Train a Model with Data that has Missing Values
medium.com
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Are you struggling with data classification accuracy? What tools can help you achieve better results? https://lnkd.in/eJeXR3M3 #RCTECHCloud #DataClassificationAccuracy #MachineLearning #TensorFlow #PyTorch #Scikit-Learn #DataPreprocessing #FeatureEngineering #AutomatedMachineLearning #ModelEvaluation #DeepLearningTechniques #DataAnnotation #Cloud-BasedMachineLearning #Domain-SpecificMachineLearningTools
Boost Data Classification Accuracy with Top Tools and Techniques
http://rctech.org
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My predefined JSON format is not good, I know. Claude improved it , by intelligently defining appropriate fields for data extraction. That is very helpful to figure out which kind of additional info to obtain from news.
Data extraction with Claude 3.5 Soonet AI
nyeimchankoko.medium.com
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We told you so. This is the way to a better self-serve interface for data, a better bot, beyond BI... This week has been a big week, not because of any development in technology in this space, but simply because of a way to measure its efficacy. Without a good way of measuring which approach is better, everything we have is simply anecdotal. What also struck me, is how well LLMs work on a semantic layer with clear names, no duplication and good descriptions included. If you don't believe LLMs will transform how we use data, here is some empirical evidence! https://lnkd.in/eE6i9uFv
We told you so
davidsj.substack.com
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Data Science enthusiast | Data Analyst | Former SDE at Amazon | Theoretical Computer Science at PSG Tech
Blog 5 Handling Imbalanced datasets - Data Cleaning (2/4) 🚀 Excited to share my latest blog post on handling imbalanced datasets in machine learning! 🤖 Imbalanced data can be a real challenge, affecting the performance of models, especially when one class dominates the other. 🤖 In this blog, I dive into common scenarios where imbalanced datasets occur and six powerful techniques to tackle this issue #DataScience #MachineLearning #ImbalancedDatasets #TechBlog https://lnkd.in/g_njvsmH
How to handle imbalanced datasets?
medium.com
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Deputy Director, Head of Data Products and Services @ DWP Digital · Digital, Data, and Technology Leader · Licensed Amateur Radio operator (G1WNZ)
Should we use LLMs to query enterprise data? A good article by Jason Ganz on the Analytics Engineering newsletter last week explores using LLMs to query enterprise data. It's a question that colleagues in all types of organisations are exploring these days, and one that we're also discussing at DWP Digital. What do you think? You will tell that I love the closing paragraph of the article, by the way: "The best way for us to figure this out? Try things! Share learnings, ideas. Talk to your peers. Ask questions. Be skeptical! And together - we’ll get there" – "Be skeptical and try things" was my mantra when I ran the NHS AI Skunkworks (with the necessary extra safety and regulatory controls in that case!) https://lnkd.in/emNinhZd
Should we even care about using LLMs to query enterprise data?
roundup.getdbt.com
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While LLM outputs receive attention, the real magic lies in data organization and retrieval techniques. Vector databases, which handle multi-dimensional data points, can be crucial for middle market companies.
Vector Database vs. Knowledge Graph: Making the Right Choice When Implementing RAG
cio.com
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AI and your SQL learning journey! Learn how to use AI tools such as Chat GPT to assist you in you path to SQL greatness. https://lnkd.in/eY_aZ2-E
The Ultimate Guide for AI-Generated Datasets for SQL
https://www.youtube.com/
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Much of the unjustified hype around complex algorithms has been caused by this: They're compared against weak baselines. For many, a “win” means beating a baseline, even if that baseline is weak. The following article discuss #technology #neuralnetworks #algorithms what a baseline is and where it fits in our data analysis projects. Enjoy the read! https://lnkd.in/ergPaKsQ
3 – Baselines
https://blog.ml.cmu.edu
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SuperRAG! Or using LLMs for helping select the best possible grounding data. A great model will not solve for poor data. Using a great model for helping get the best possible data https://lnkd.in/dCMvxzyA
SuperRAG – How to achieve higher accuracy with Retrieval Augmented Generation
techcommunity.microsoft.com
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