Scott G. Parent’s Post

20X Mentor-Mentee Matching with Posit Team 🔥 Regis A. James @ Regeneron shared with me how they use open source data science and Posit to build their matchmaker AI platform that automatically connects mentors and mentees to share knowledge across the Fortune 500 company. Love getting to share use cases like this one! Listen to more of the story here: https://lnkd.in/eNq2YF5W Huge kudos to Regis A. James, Jordan Leao, and Jennifer (Galarza) Michel for bringing this idea to life! Just a short snippet below!

Join me & Uri Goren for this great event! I'll review the recent advancements in time series foundation models and Uri will give us an introduction to vector databases and share from his vast experience deploying deep learning applications to production can't wait to see you all!!!

Oasis Infobyte Task 1 🌺 Excited to share my recent project where I utilized the Iris dataset to classify flower species using machine learning techniques! 🌿🔍 By analyzing factors like sepal width and petal length, we successfully built a model to accurately predict flower types. 💡 Let’s continue to explore the fascinating intersection of data science and nature! Dataset used 1.Iris dataset Libraries used 1.Pandas 2.Numpy 3.Seaborn 4.Matplot 5.Sklearn Classification Algorithm used 1.Naive Bayes Classifer #oasisinfobyte #datascience #Machinelearning

Thinking about what to watch next? 📺 Superlinked got your back 😅 Check out the recording of Mór's hands-on workshop on building RAG solutions with complex data, in collaboration with Data Science Festival 🤓 Learn how to - 📖 Embed timestamp, numeric “helpfulness” rating and unstructured text data 📖 Apply weights at query time to increase the quality and consistency of the results 📖 Set up a RAG system from your data to power a chatbot 📖 Connect to a vector DB and a simple deployment to run your application Leave your questions in the comments 🙌 #datascience #RAG #LLM #GenAI #webinar

A pretty nice crash-course on DARTS features for time series forecasting, implementing the NBEATS zero-shot forecasting model, as well as deep learning and classical machine learning models with a scikit-learn approach! https://lnkd.in/e2AyABz3 #darts #unit8 #timeseries #forecasting #machinelearning #ai #deeplearning #scikitlearn #python

I just completed the final project for the *Data Seekho* 3-Day ML Bootcamp: I built a model from scratch using the heart disease dataset, applying everything I learned about data preprocessing, feature engineering, and classification algorithms. It was amazing to see the model predict heart disease with real data. I'm excited to take these skills further! 💻🚀 *— Umama Masroor #DataSeekho #DataSeekho1Million #SelfChallenge #MachineLearning #HeartDiseasePrediction #DataScience #FeatureEngineering #DataPreprocessing #ClassificationAlgorithms #MLBootcamp #DataScienceSkills #PredictiveModeling #AIInHealthcare

I just completed the final project for the *Data Seekho* 3-Day ML Bootcamp: I built a model from scratch using the heart disease dataset, applying everything I learned about data preprocessing, feature engineering, and classification algorithms. It was amazing to see the model predict heart disease with real data. I'm excited to take these skills further! 💻 Would soon share my first data analysis project with you all. #DataSeekho #DataSeekho1Million #SelfChallenge #MachineLearning #HeartDiseasePrediction #DataScience #FeatureEngineering #DataPreprocessing #ClassificationAlgorithms #MLBootcamp #DataScienceSkills #PredictiveModeling #AIInHealthcare

👀 Highly recommend checking out the upcoming Causal Data Science Meetup event on Augmenting LLMs with Causal Reasoning! 

This Pi Day (March 14th, 2024), we rolled out a small challenge at the workplace to see who could name the most digits of Pi. And let us tell you, we had fun about it. Enjoy this video of those responses. Here's to pi – the number that never ends and the constant that always amazes us. Keep on celebrating, folks! #Ganit #GanitInc #DataSpeaks #mathematics #fun #workplace #culture #math #data #datascience #pi #piday #AI #ML #analytics #dataanalytics #dataanalystjobs #play 

The decision is taken by ML algorithms using concepts such as entropy ,information gain, if you see from the below image we have two pictures [low entropy, High entropy] Entropy :it is all about how uncertain the data is how distorted the data is how we can decrease entropy. -In the case of low entropy we will get high information gain, we will get to know how the data is among the features -But if you see in the other image of high entropy we can see clumsy data plot ,we cannot get any insights from data ,the which is difficult to use.