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#datascience

52 publicaciones48 participantes8 publicaciones hoy
Mortylen

My new article about k-NN classification, covering everything from data preprocessing to performance evaluation.

In the article, I discuss:
- Data preprocessing (scaling, cleaning, input preparation).

- Distance metrics for k-NN: Euclidean, Manhattan and Minkowski.

- Evaluation metrics: Accuracy, Precision, Recall and F1-score.

Bonus feature: the ability to set individual weights for each feature 🤫

I hope the article will be useful and maybe even a bit inspiring.

🔹 mortylen.hashnode.dev/k-nn-cla

#machinelearning#datascience#opensource
*
JRelland :linux: :flower:

Chaque jour, de nouveaux défis sont à relever en matière de santé, d'environnement, d'inclusion sociale, d'éducation ou de citoyenneté et, aujourd'hui, j'ai décidé de m'engager.
Je suis bénévole chez @Data For Good !
Ainsi une communauté de 3600**+ volontaires** tech souhaitant mettre leurs compétences (Data, Dev, IA, UX/UI) au profit d'associations, d'ONG, et de l'ESS - et pour l'intérêt général.

dataforgood.fr/ !

#dataforgood
@oisux @gul_lolica
#dataviz #datascience #opendata

dataforgood.frHome | Data For GoodData For Good - le numérique pour l'intérêt général
DSLC Videos

From the @DSLC :rstats:​chives:

:rstats: Learning Statistics with R: Comparing two means. youtu.be/-_KYR_S4-iA #RStats #statistics

:python: Practical Deep Learning for Coders: Attention & transformers youtu.be/de7EGEX3nhs #AI #DeepLearning #PyData

:rstats: ShinyUI: Web application concepts youtu.be/ibvWVALp19M #RStats #rshiny

Visit dslc.video for hours of new #DataScience videos every week!

YouTubeLearning Statistics with R: Comparing two means. (lsr01 13)Por Data Science Learning Community Videos
Harris Georgiou

Week 61: Real-world data | #DSbook #writingprocess #datascience

Degraded data quality is one of the first issue a Data Science team will identify and face at the beginning of a project. No matter how much the data provider tries to avoid errors and inconsistencies, sources and sensors are always unreliable in the long-term.

For more details, check out:
apneacoding.blogspot.com/2024/

#dataanalytics#machinelearning#artificialintelligence
Rami Krispin :unverified:

My weekly newsletter is out!

This week:
🔹 Open Source of the Week - The PandasAI project
🔹 New learning resources - Getting started with Docker Model Runner, MCP Dev days, context engineering, training Qwen 3 model, fine-tuning local LLMs
🔹 Book of the week - Learning SQL by Alan Beaulieu

📌 Join 30k subscribers and subscribe for weekly updates.

ramikrispin.substack.com/p/the

Rami's Data Newsletter · The PandasAI Project, Learning SQL Book, Fine-Tuning Local LLMsPor Rami Krispin
#ai#sql#opensource
DSLC Videos

Recent @DSLC club meetings:

:rstats:python: Devops for Data Science: Environments as Code {YOUTUBELINK}

From the @DSLC :rstats:​chives:

:python: :rstats: Mastering Shiny: Layout, themes, HTML youtu.be/H7fjO6f-8xs #PyData #PyShiny #RShiny #RStats

:rstats: Advanced R: S4 youtu.be/M8Poajmj-HU #RStats

:rstats: Bayes Rules! Extending the Normal Regression Model youtu.be/r1EVL_g2TX4 #RStats

Visit dslc.video for hours of new #DataScience videos every week!

YouTubeMastering Shiny: Layout, themes, HTML (mshiny07 6)Por Data Science Learning Community Videos
Syed Amer

👋 Hello Mastodon! Excited to be here.
I’m a Data Science Expert specializing in Generative AI & Machine Learning. Skilled in Python, R, MySQL, Power BI & Analytics.
I’ll share insights, projects & discussions on AI, ML, Data Science & Visualization. Looking forward to connecting & learning with this community 🚀

#Introduction#NewHere#DataScience
Mortylen

I’ve Started a New Machine Learning Project

I just launched a small project focused on machine learning algorithms and metrics. I originally started it to better organize my knowledge and learn new things, but I realized it could be useful for others as well, so I decided to make it public.

The goal is to help with choosing the right algorithm for different tasks, with detailed explanations and practical code examples. The project is still in its early stages (apologies for any mistakes), but I hope it’ll be useful to someone.

Feedback is very much appreciated as it continues to evolve. Feel free to share your thoughts or suggestions!

🔹 mlcompassguide.dev/

ML Compass Guide · ML Compass GuideML Compass Guide is your interactive companion for navigating the world of machine learning algorithms. Whether you're a beginner or a seasoned developer, this guide helps you choose the right algorithm based on your data, task type, and constraints. Follow clear decision paths, explore algorithm explanations, view real-world use cases, and compare resource requirements.
#machine_learning#AI#opensource
DSLC Videos

Recent @DSLC club meetings:

:rstats: Fundamentals of Data Visualization: & Chapter 11 Visualizing nested proportions youtu.be/9bsj89Ua5XM #RStats #PyData #DataViz

From the @DSLC :rstats:​chives:

:rstats: Tidytext: Converting to and from non-tidy formats youtu.be/Rf-NBtAkKP0 #RStats

:rstats: Engineering Production Grade Shiny Apps Book Club youtu.be/p5IXw-AlBK4 #RStats

Visit dslc.video for hours of new #DataScience videos every week!

YouTubeFundamentals of DataViz: Visualizing proportions & Visualizing nested proportions (dataviz01 10 11)Por Data Science Learning Community Videos
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