kashif lashari’s Post

My Day 1 of building consistency. For clarity purpose, SQL is a standard language for managing, manipulating, and interacting with data in relational databases. It allows users to perform tasks such as querying, updating, and managing data in databases. I achieved today's task by following these steps; First: I got a dataset from Kaggle. Second: I uploaded it to my sqlite workspace. Third: I generated use cases using generative AI. Fourth: I solved the problem. Here's a breakdown of the task: Identify the 10 top-selling products based on the number of items sold. I used CTE to simplify the main query by calculating the number of items sold and total revenue for each product in a separate step, making the main query easier to read and maintain. Also, I used the row_number function to assign a sequential rank to each product based on the number of items sold. #SQL #DataAnalytics

Let's talk #vectorsearch and how it's revolutionising the search landscape! Remember the days of clunky SQL queries searching for exact matches? Vector search throws that out the window and lets us find data based on meaning and relationships! Here's why it's a game-changer for LLMs (especially for that RAG project ): ** Improved Relevance:** LLMs trained on massive amounts of text data can now find similar ideas and concepts way faster, leading to super-relevant responses! Think of it like having a mind-reading superpower for your LLM ⚖️ Reduced Bias:  LLMs can get stuck in their training data echo chamber. Vector search helps them explore a wider range of info, leading to more balanced and unbiased responses! ⏩ Faster Training:  Finding similar data points in a snap? Vector search speeds up LLM training, freeing up valuable engineer time for even cooler projects. I am trying out vector search from AWS Elastic Search for my RAG project. Let me know how your are using vector search in your projects in the comment section. #rag #llm #vectorsearch #ai

""" TOWARD Full-Stack Data Science Learning FastAPI """ I am honored to be speaking soon at DataHour with Analytics Vidhya. You may have seen some recent posts of mine about working TOWARD Full-Stack #datascience and some tools that I've encouraged learning so that it's possible for you to create and/or deliver your own #data #analytics and #artificialintelligience tools, apps, and app archictectures. This session will be a demo of a recent post of mine on building a basic FastAPI based REST API. I consider FastAPI an important tool for us to learn in that journey TOWARD Full-Stack DS, ML, and AI. I hope that you can attend -> https://shorturl.at/boAF5

Machine Learning Quick Reference (Cheat Sheet) ↳ The cheat sheet provides a quick guide on essential Machine Learning algorithms, covering data preparation, algorithm specifics, and optimization techniques. ↳ Data preparation involves rescaling inputs, handling missing data, and addressing the Curse of Dimensionality through dimensionality reduction or feature selection. Removing outliers is crucial for algorithms like AdaBoost. ↳ Support Vector Machines (SVM) are highlighted for their high performance with minimal tuning. SVM uses hyperplanes to separate data points by class, with support vectors and parameter C playing significant roles. Various kernel options like linear, polynomial, and radial are available, with numeric input requirements often necessitating dummy transformation for categorical features. ↳ Ensemble algorithms, including Bagging, Random Forest, and AdaBoost, enhance performance by combining multiple simpler algorithms. Random Forest, a part of the Bagging technique, is particularly noted for its effectiveness. ↳ Optimization is fundamental in machine learning, with Gradient Descent commonly used to minimize cost functions like Mean Squared Error (MSE) or Sum of Squared Residuals (SSR). ↳ Additional resources are recommended for further learning and reference, including Machine Learning Mastery, Scikit-learn website, Probability Cheatsheet by W. Chen, HackingNote, and Seattle Data Guy blog. Unlock the power of machine learning with this quick reference guide, designed to streamline your learning and application of key concepts and techniques. ✅ Get any Data science training videos, https://lnkd.in/gQVwVNSG ✅ Subscribe to our Youtube channel: https://lnkd.in/gD54ZjUh ✅ P.S. Want to Upskill your Data Science workforce? Check out our course catalog for corporate training, https://lnkd.in/dYipv_Qm #datascience, #machinelearning, #ai, #bigdata, #analytics, #datascientist, #deeplearning, #python

10 most common loss functions in #MachineLearning in a single frame. Loss functions are a key component of ML algorithms. Thus, knowing about the most common loss functions in machine learning is extremely crucial. #MachineLearning #DataScience #LossFunctions #AI #DeepLearning #Data #Analytics #Statistics #Training #Errors #onlinelearning #google #coursera 𝗗𝗼𝗻'𝘁 𝗴𝗲𝘁 𝗹𝗲𝗳𝘁 𝗯𝗲𝗵𝗶𝗻𝗱 - 𝗳𝗿𝗲𝗲 𝗰𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗼𝗿 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗔𝗜: Knowing how to use AI best is the skill of the future. 🔹 7000+ Courses Free Access: https://lnkd.in/dc7dUxkj ➣ IBM Introduction to Artificial Intelligence (AI) 🔗 https://lnkd.in/d2Awst5W ➣ Google AI Essentials 🔗https://lnkd.in/gQ_G9656 ➣ Google Data Analytics: 🔗 https://lnkd.in/dPe_2gbX ➣ Google Advanced Data Analytics: 🔗 https://lnkd.in/dDYEjEjk ➣ Google IT Automation with Python 🔗 https://lnkd.in/ddWaknMr ➣ IBM Data Engineering: 🔗 https://lnkd.in/dTfhTwYq ➣ Google Digital Marketing & E-commerce https://lnkd.in/dv6xeHge ➣ ML Pipelines 🔗 https://lnkd.in/gkWHWiJE ➣ Fundamentals of Generative AI: 🔗 https://lnkd.in/g334VaJd ➣ IBM Applied AI: 🔗 https://lnkd.in/denDFBMw ➣ Intro to Data Science: 🔗 https://lnkd.in/dSBDhvBp ➣ Data Science Fundamental with Python & SQL: 🔗 https://lnkd.in/dwvzjedi ➣ IBM AI Engineering : 🔗 https://lnkd.in/dh2aVyGW ➣ Google Business Intelligence 🔗 https://lnkd.in/dkqayccr

Here are 12 must-read blogs that every data scientist should follow and read! Staying updated with the latest trends and techniques in data science is crucial for anyone in the field. 1. Towards Data Science: Great for tutorials and insights from industry experts. Link: (https://lnkd.in/dfnxg3Zp) 2. KDnuggets: A hub for data mining and machine learning. Link: (https://www.kdnuggets.com) 3. Data Science Central: Community-driven content on big data and analytics. Link: (https://lnkd.in/dxC6aTih) 4. Analytics Vidhya: Perfect for beginners and advanced learners alike. Link: (https://lnkd.in/dFc7rJxv) 5. Towards AI: Covers a broad range of AI and ML topics. Link: (https://www.towardsai.net) 6. DataCamp Blog: Insights, tutorials, and news in the data science space. Link: (https://lnkd.in/dSDJ3TJS) 7. Distill: Deep dives into machine learning research with interactive visuals. Link: (https://distill.pub) 8. Fast.ai: Practical insights into deep learning and AI. Link: (https://www.fast.ai/blog) 9. R-bloggers: A must-read for R enthusiasts. Link: (https://www.r-bloggers.com) 10. Machine Learning Mastery: Hands-on tutorials for mastering ML and DL. Link: (https://lnkd.in/d-fVDmVg) 11. Google AI Blog: Updates and insights from Google's AI research. Link: (https://ai.googleblog.com) 12. OpenAI Blog: Latest advancements in AI, straight from OpenAI. Link: (https://openai.com/blog) If you find these helpful, feel free to... 👍 React ♻️ Share 💬 Comment #datascience #blogs

A very useful list of blogs. thanks for compiling and sharing Venkata Naga Sai Kumar Bysani

📰 News everyone! 📰 Exciting update! I've just released a new blog post titled "MInference." 🎉 Are you a data scientist, machine learning enthusiast, or simply intrigued about the future of AI? This post offers valuable insights on the new open-source project from Microsoft called MInference: Million-Tokens Prompt Inference for Long-context LLMs. I'm eager to hear your thoughts and feedback! Let's engage in a conversation on how MInference can revolutionise our approach to model interpretation. 🤖💡 Additionally, the Research section now features two new papers: - AI Agents that matter. - RouteLLM - Learning to Route LLMs with preference data. Stay updated on cutting-edge technology and AI advancements by following me! #MachineLearning #AI #DataScience #MInference #TechInnovation #BlogPost #Microsoft

𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗙𝗼𝗿𝗺𝘂𝗹𝗮𝘀 𝗳𝗼𝗿 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 ✨ Want to level up your machine-learning skills? Statistical formulas are the building blocks for understanding and applying machine learning algorithms effectively. 𝗧𝗵𝗶𝘀 𝘁𝗮𝗯𝗹𝗲 𝗽𝗿𝗼𝘃𝗶𝗱𝗲𝘀 𝗮 𝗾𝘂𝗶𝗰𝗸 𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝘀𝗼𝗺𝗲 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗳𝗼𝗿𝗺𝘂𝗹𝗮𝘀, 𝗶𝗻𝗰𝗹𝘂𝗱𝗶𝗻𝗴: * Population vs Sample Measures * Variance and Standard Deviation * Confidence Intervals * Hypothesis Testing Bookmark this post for easy reference, and let me know in the comments which formula you find most useful in your machine-learning work! #MachineLearning #Statistics #DataScience #MachineLearning #DataAnalysis #AI #Python #DeepLearning #BigData Don't get left behind - free courses for learning AI: Knowing how to best use AI is the skill of the future. 🔹 7000+ Course Free Access: https://lnkd.in/dc7dUxkj 🔺Google Ai Essentials https://lnkd.in/gQ_G9656 ➤ Google Data Analytics: https://lnkd.in/dPe_2gbX 🔺Google Advanced Data Analytics: https://lnkd.in/dDYEjEjk ➤ Google IT Automation with Python https://lnkd.in/ddWaknMr 🔺 IBM Data Engineering: https://lnkd.in/dTfhTwYq ➤ Google Digital Marketing & E-commerce https://lnkd.in/dv6xeHge 🔹 ML Pipelines https://lnkd.in/gkWHWiJE 🔺Fundamentals of Generative AI: https://lnkd.in/g334VaJd 🔹 IBM Applied AI: https://lnkd.in/denDFBMw 🔺 Intro to Data Science: https://lnkd.in/dSBDhvBp 🔹 Data Science Fundamental with Python & SQL: https://lnkd.in/dwvzjedi 🔺 IBM AI Engineering : https://lnkd.in/dh2aVyGW 🔹 Google Business Intelligence https://lnkd.in/dkqayccr

Unlock the power of machine learning with this comprehensive quick reference guide, carefully designed to enhance your understanding and application of key concepts and techniques in Data Science Technology. From essential Machine Learning algorithms to optimization techniques, this cheat sheet covers it all. For further learning and reference, recommended resources are also provided. Dive in and elevate your skills in AI, ML, and RPA. #ThoughtLeadership #DataScience #MachineLearning #ArtificialIntelligence #SkillDevelopment #DataScienceTraining