Blue Headline - Tech News<p>🚗 GPUs can now accelerate vehicle intrusion detection by up to 159x compared to CPUs.<br>That’s not a tweak—it’s a leap.</p><p>A new study dives into how libraries like cuML outperform scikit-learn in real-time IoV security applications, all while maintaining accuracy.</p><p>Could this reshape how we secure connected vehicles at the edge?</p><p>🔗 Dive into the details: <a href="https://blueheadline.com/tech-news/gpu-faster-intrusion-detection-iov/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">blueheadline.com/tech-news/gpu</span><span class="invisible">-faster-intrusion-detection-iov/</span></a></p><p><a href="https://mastodon.social/tags/Technology" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Technology</span></a> <a href="https://mastodon.social/tags/CyberSecurity" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CyberSecurity</span></a> <a href="https://mastodon.social/tags/IoV" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>IoV</span></a> <a href="https://mastodon.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>MachineLearning</span></a> <a href="https://mastodon.social/tags/EdgeComputing" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>EdgeComputing</span></a> <a href="https://mastodon.social/tags/AI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AI</span></a> <a href="https://mastodon.social/tags/GPUAcceleration" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>GPUAcceleration</span></a> <a href="https://mastodon.social/tags/cuML" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>cuML</span></a> <a href="https://mastodon.social/tags/ScikitLearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ScikitLearn</span></a> <a href="https://mastodon.social/tags/BlueHeadline" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>BlueHeadline</span></a></p>
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LavX News<p>Future-Proof Your Engineering Career: Essential Skills for 2025 Revealed</p><p>LinkedIn's latest report outlines the top engineering skills in demand for 2025, highlighting the growing importance of AI and cloud computing expertise. As the tech landscape evolves, understanding t...</p><p><a href="https://news.lavx.hu/article/future-proof-your-engineering-career-essential-skills-for-2025-revealed" rel="nofollow noopener noreferrer" target="_blank"><span class="invisible">https://</span><span class="ellipsis">news.lavx.hu/article/future-pr</span><span class="invisible">oof-your-engineering-career-essential-skills-for-2025-revealed</span></a></p><p><a href="https://mastodon.cloud/tags/news" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>news</span></a> <a href="https://mastodon.cloud/tags/tech" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>tech</span></a> <a href="https://mastodon.cloud/tags/LargeLanguageModels" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>LargeLanguageModels</span></a> <a href="https://mastodon.cloud/tags/AzureSQL" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AzureSQL</span></a> <a href="https://mastodon.cloud/tags/ScikitLearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ScikitLearn</span></a></p>
Technology Tales<p>Discover 10 effective Python one-liners for Scikit-learn that simplify your <a href="https://mstdn.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>MachineLearning</span></a> tasks. These short snippets handle key operations like data import, splitting datasets, standardising features, PCA dimensionality reduction, and training SVM classifiers. Perfect for rapid experiments and cleaner code. <a href="https://mstdn.social/tags/Python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Python</span></a> <a href="https://mstdn.social/tags/DataScience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DataScience</span></a> <a href="https://mstdn.social/tags/SoftwareDevelopment" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>SoftwareDevelopment</span></a> <a href="https://mstdn.social/tags/ScikitLearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ScikitLearn</span></a> <a href="https://mstdn.social/tags/AI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AI</span></a> <a href="https://www.kdnuggets.com/10-python-one-liners-for-scikit-learn" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">kdnuggets.com/10-python-one-li</span><span class="invisible">ners-for-scikit-learn</span></a></p>
Valeriy M., PhD, MBA, CQF<p>The real lesson here? **Question default “best practices”**—because many of them exist not because they’re good, but because they’re easy to copy-paste into a framework. </p><p><a href="https://sigmoid.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>MachineLearning</span></a> <a href="https://sigmoid.social/tags/ConformalPrediction" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ConformalPrediction</span></a> <a href="https://sigmoid.social/tags/PlattScaling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PlattScaling</span></a> <a href="https://sigmoid.social/tags/AI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AI</span></a> <a href="https://sigmoid.social/tags/ScikitLearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ScikitLearn</span></a> <a href="https://sigmoid.social/tags/Calibration" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Calibration</span></a></p>
laguill<p>Hi <a href="https://fosstodon.org/tags/FediHelp" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>FediHelp</span></a> 👋<br>As I am learning <a href="https://fosstodon.org/tags/scikitlearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>scikitlearn</span></a> do you have any resources to work with knn_imputer ?</p><p>I want to replace NAN values.</p><p>How do you<br>- Select optimized n_neighbors<br>- Visualize what the imputer do with plots or metrics</p><p>Any link to blog post or tutorials are welcome 🙂<br>Thanks</p><p><a href="https://fosstodon.org/tags/Python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Python</span></a> <a href="https://fosstodon.org/tags/DataScience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DataScience</span></a></p>
Alex Zap<p><a href="https://mastodon.social/tags/python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>python</span></a> <a href="https://mastodon.social/tags/ai" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ai</span></a> <a href="https://mastodon.social/tags/machinelearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>machinelearning</span></a> <br><a href="https://mastodon.social/tags/mlops" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>mlops</span></a> <a href="https://mastodon.social/tags/realtime" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>realtime</span></a> <a href="https://mastodon.social/tags/neuralnetwork" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>neuralnetwork</span></a> <a href="https://mastodon.social/tags/scikitlearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>scikitlearn</span></a> </p><p>Continuous Machine Learning (CML): Basics & Best Practices for Adoption<br>The systematic way of learning continually from real-time data.<br>Plot: CML Confusion Matrix<br><a href="https://mastodon.social/tags/exploremore" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>exploremore</span></a> 👇 <br><a href="https://medium.com/@alexzap922/continuous-machine-learning-cml-basics-best-practices-for-adoption-257c19fbcbe9?sk=98bf072ddaccccd67ba83686261d8b15" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">medium.com/@alexzap922/continu</span><span class="invisible">ous-machine-learning-cml-basics-best-practices-for-adoption-257c19fbcbe9?sk=98bf072ddaccccd67ba83686261d8b15</span></a></p>
Alex Zap<p><a href="https://mastodon.social/tags/python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>python</span></a> <a href="https://mastodon.social/tags/algorithm" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>algorithm</span></a> <a href="https://mastodon.social/tags/scikitlearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>scikitlearn</span></a> <br><a href="https://mastodon.social/tags/machinelearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>machinelearning</span></a> <a href="https://mastodon.social/tags/artificialintelligence" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>artificialintelligence</span></a> <br><a href="https://mastodon.social/tags/technology" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>technology</span></a> <br><a href="https://mastodon.social/tags/regression" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>regression</span></a> <br><a href="https://mastodon.social/tags/guide" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>guide</span></a> <a href="https://mastodon.social/tags/tutorial" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>tutorial</span></a> <br>👉 A Comprehensive Guide to 85 Supervised Machine Learning Algorithms in Scikit-Learn — Part 1. Regressors</p><p> 👉 Best Practices in Building & Training ML Models with 51 Regressors (Codes, Plots, and More)</p><p>Master all-in-one AI concepts and develop hands-on ML skills with one of the most popular and powerful libraries for ML in Python!</p><p><a href="https://mastodon.social/tags/exploremore" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>exploremore</span></a> 👇 </p><p><a href="https://medium.com/@alexzap922/a-comprehensive-guide-to-85-supervised-machine-learning-ml-algorithms-in-scikit-learn-part-1-9168aee3cf2a?sk=712ecb27162654086ea9780a0d19a5be" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">medium.com/@alexzap922/a-compr</span><span class="invisible">ehensive-guide-to-85-supervised-machine-learning-ml-algorithms-in-scikit-learn-part-1-9168aee3cf2a?sk=712ecb27162654086ea9780a0d19a5be</span></a></p>
Software Heritage<p>🇫🇷 For the Francophones: Check out this overview of <span class="h-card" translate="no"><a href="https://mstdn.social/@swheritage" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>swheritage</span></a></span> + <a href="https://mstdn.social/tags/Scikitlearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Scikitlearn</span></a> in Le Monde: <a href="https://archive.is/BRqAo" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">archive.is/BRqAo</span><span class="invisible"></span></a></p>
Eric Maugendre<p><span class="h-card" translate="no"><a href="https://a.gup.pe/u/data" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>data</span></a></span> <span class="h-card" translate="no"><a href="https://a.gup.pe/u/datadon" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>datadon</span></a></span> 🧵</p><p>Redressing <a href="https://hachyderm.io/tags/Bias" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bias</span></a>: "Correlation Constraints for Regression Models":<br>Treder et al (2021) <a href="https://doi.org/10.3389/fpsyt.2021.615754" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">doi.org/10.3389/fpsyt.2021.615</span><span class="invisible">754</span></a></p><p><a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>linearRegression</span></a> <a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>modeling</span></a> <a href="https://hachyderm.io/tags/probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probability</span></a> <a href="https://hachyderm.io/tags/probabilities" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probabilities</span></a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statistics</span></a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>stats</span></a> <a href="https://hachyderm.io/tags/modelling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>modelling</span></a> <a href="https://hachyderm.io/tags/regression" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>regression</span></a> <a href="https://hachyderm.io/tags/correctionRatio" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>correctionRatio</span></a> <a href="https://hachyderm.io/tags/skLearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>skLearn</span></a> <a href="https://hachyderm.io/tags/scikitLearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>scikitLearn</span></a> <a href="https://hachyderm.io/tags/python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>python</span></a> <a href="https://hachyderm.io/tags/AIDev" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AIDev</span></a></p>
Steve Duncan<p>Sometimes I wonder how many other people working in some business are learning <a href="https://mastodon.social/tags/python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>python</span></a> and <a href="https://mastodon.social/tags/scikitlearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>scikitlearn</span></a> because someone said “We need better <a href="https://mastodon.social/tags/forecasting" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>forecasting</span></a>” and is performing the n+1th analysis of industry metrics. We think we’re blazing a new trail but it’s really just a shortcut through the vacant lot on the way to school.</p>
Peter Jachim<p>I just did my first project using the <a href="https://mastodon.world/tags/mlflow" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>mlflow</span></a> library to track metrics on iterations of manual tuning of an <a href="https://mastodon.world/tags/sklearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>sklearn</span></a> pipeline, it works great and gives me some idea of the search space before moving into automated hyperparameter tuning.</p><p>I am using it in a super basic way, as an alternative to creating a gazillion cells with comments tracking metrics, does anyone have any favorite features to check out for taking mlflow to the next level?<br><a href="https://mastodon.world/tags/machinelearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>machinelearning</span></a> <a href="https://mastodon.world/tags/python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>python</span></a> <a href="https://mastodon.world/tags/MLOps" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>MLOps</span></a> <a href="https://mastodon.world/tags/scikitlearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>scikitlearn</span></a></p>
ViOffice<p>Nachdem wir die Gemeinsamkeiten von <a href="https://mastodon.cloud/tags/DataScience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DataScience</span></a>, <a href="https://mastodon.cloud/tags/DataAnalytics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DataAnalytics</span></a> sowie <a href="https://mastodon.cloud/tags/DataEngineering" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DataEngineering</span></a> betrachtet haben, möchten wir heute auf die Unterschiede der Disziplinen eingehen 💡</p><p>▶️ Data Science: Entwicklung von Modellen & <a href="https://mastodon.cloud/tags/Algorithmen" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Algorithmen</span></a> zur Vorhersage zukünftiger <a href="https://mastodon.cloud/tags/Trends" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Trends</span></a> oder zur <a href="https://mastodon.cloud/tags/Automatisierung" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Automatisierung</span></a> von Prozessen (<a href="https://mastodon.cloud/tags/Python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Python</span></a>, <a href="https://mastodon.cloud/tags/R" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>R</span></a>, <a href="https://mastodon.cloud/tags/TensorFlow" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>TensorFlow</span></a>, <a href="https://mastodon.cloud/tags/scikitlearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>scikitlearn</span></a>) 📈</p><p>▶️ Data Analytics: <a href="https://mastodon.cloud/tags/Analyse" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Analyse</span></a> & Interpretation von <a href="https://mastodon.cloud/tags/Daten" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Daten</span></a>, um handlungsrelevante Erkenntnisse zu liefern (<a href="https://mastodon.cloud/tags/Excel" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Excel</span></a>, <a href="https://mastodon.cloud/tags/Tableau" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Tableau</span></a>, <a href="https://mastodon.cloud/tags/PowerBI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PowerBI</span></a>, <a href="https://mastodon.cloud/tags/SQL" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>SQL</span></a>, Python) 📊</p><p>1/2</p>
robrich<p><a href="https://www.docker.com/blog/supercharging-ai-ml-development-with-jupyterlab-and-docker/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">docker.com/blog/supercharging-</span><span class="invisible">ai-ml-development-with-jupyterlab-and-docker/</span></a> - want to play with <a href="https://hachyderm.io/tags/DataScience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DataScience</span></a> and <a href="https://hachyderm.io/tags/AI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AI</span></a>? <a href="https://hachyderm.io/tags/Jupyter" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Jupyter</span></a> in a <a href="https://hachyderm.io/tags/Docker" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Docker</span></a> <a href="https://hachyderm.io/tags/container" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>container</span></a> is a great way to go. Nice walk-through of <a href="https://hachyderm.io/tags/scikitlearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>scikitlearn</span></a>, <a href="https://hachyderm.io/tags/matplotlib" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>matplotlib</span></a>, and <a href="https://hachyderm.io/tags/Python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Python</span></a> Masahito Zembutsu.</p>
Joe Steinbring :thisisfine:<p><span class="h-card" translate="no"><a href="https://fosstodon.org/@sirber" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>sirber</span></a></span> In the market that I am in, there are almost no postings for <a href="https://toot.works/tags/php" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>php</span></a> devs but a heck of a lot for <a href="https://toot.works/tags/python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>python</span></a>. I've been exploring <a href="https://toot.works/tags/scikitlearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>scikitlearn</span></a> in particular with python because that seems to be getting hot.</p>
Alex Zap<p><a href="https://mastodon.social/tags/Python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Python</span></a> <a href="https://mastodon.social/tags/DataScience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DataScience</span></a> <a href="https://mastodon.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>MachineLearning</span></a> <a href="https://mastodon.social/tags/Regression" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Regression</span></a> <a href="https://mastodon.social/tags/Amsterdam" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Amsterdam</span></a><br><a href="https://mastodon.social/tags/RealEstate" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>RealEstate</span></a> <a href="https://mastodon.social/tags/REIT" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>REIT</span></a><br><a href="https://mastodon.social/tags/dataanalytics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataanalytics</span></a> <a href="https://mastodon.social/tags/automation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>automation</span></a><br><a href="https://mastodon.social/tags/NLP" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>NLP</span></a> <a href="https://mastodon.social/tags/plotly" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>plotly</span></a> maps<br><a href="https://mastodon.social/tags/Airbnb" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Airbnb</span></a> <a href="https://mastodon.social/tags/Perarius" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Perarius</span></a> <a href="https://mastodon.social/tags/scikitlearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>scikitlearn</span></a> <a href="https://mastodon.social/tags/autoviz" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>autoviz</span></a> <a href="https://mastodon.social/tags/itables" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>itables</span></a><br><a href="https://mastodon.social/tags/sweetviz" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>sweetviz</span></a> <a href="https://mastodon.social/tags/tuning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>tuning</span></a> <a href="https://mastodon.social/tags/optimization" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>optimization</span></a> <a href="https://mastodon.social/tags/prices" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>prices</span></a> <a href="https://mastodon.social/tags/reviews" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>reviews</span></a> <a href="https://mastodon.social/tags/listings" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>listings</span></a> <a href="https://mastodon.social/tags/kaggle" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>kaggle</span></a> <a href="https://mastodon.social/tags/dataset" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataset</span></a></p><p><a href="http://newdigitals.org/2024/04/04/python-data-science-for-real-estate-reit-amsterdam-auto-eda-nlp-maps-ml/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">http://</span><span class="ellipsis">newdigitals.org/2024/04/04/pyt</span><span class="invisible">hon-data-science-for-real-estate-reit-amsterdam-auto-eda-nlp-maps-ml/</span></a></p>
Peng Qian<p>🔍 Dive into the world of <a href="https://me.dm/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>MachineLearning</span></a> with our latest guide on scikit-learn's Display API! Say goodbye to complex Matplotlib code and hello to easy, insightful model visualizations. Perfect for beginners! 📊✨<br>Read more ➡️<br><a href="https://www.dataleadsfuture.com/scikit-learn-visualization-guide-making-models-speak/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">dataleadsfuture.com/scikit-lea</span><span class="invisible">rn-visualization-guide-making-models-speak/</span></a></p><p><a href="https://me.dm/tags/DataScience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DataScience</span></a> <a href="https://me.dm/tags/AI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AI</span></a> <a href="https://me.dm/tags/ML" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ML</span></a> <a href="https://me.dm/tags/Visualizations" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Visualizations</span></a> <a href="https://me.dm/tags/scikitlearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>scikitlearn</span></a> <a href="https://me.dm/tags/DataViz" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DataViz</span></a></p>
Daniel Pelliccia<p>📚 My new post has just landed on the Nirpy Research website!</p><p>The post is about Robust PCA (Principal Component Analysis), which is an implementation of the PCA algorithm that is robust against outliers in the dataset.</p><p>📝 Here are the main takeaway points:</p><p>🔸 Outliers in the data can significantly distort the principal components calculated by the standard PCA algorithm.</p><p>🔸 The approach based on calculating the eigenvectors of the covariance matrix allows for a robust implementation of PCA by using a robust estimation of the covariance matrix that is less sensitive to outliers.</p><p>🔸 In <a href="https://aus.social/tags/python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>python</span></a> and <a href="https://aus.social/tags/ScikitLearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ScikitLearn</span></a>, the MinCovDet function can be used to make a robust estimate of the covariance matrix, which can then be used to calculate the principal components in a way that is not affected by outliers.</p><p>🔸 The standard deviation estimation itself is not robust to outliers. Therefore, for proper data scaling before <a href="https://aus.social/tags/PCA" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PCA</span></a>, the robust standard deviation should be calculated using the covariance matrix estimated by the MinCovDet function.</p><p>🌐 Check out the post for more info and the Python code</p><p><a href="https://nirpyresearch.com/robust-pca/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="">nirpyresearch.com/robust-pca/</span><span class="invisible"></span></a> </p><p><a href="https://aus.social/tags/DataCleaning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DataCleaning</span></a> <a href="https://aus.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>MachineLearning</span></a> <a href="https://aus.social/tags/spectroscopy" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>spectroscopy</span></a></p>
synlogic<p>anyone know of a FOSS lib equiv to Python's Scikit-learn (sklearn) but in/for Go?</p><p>(and to forestall an obvious suggestion which is likely a non-starter for my needs: yes I am aware of idea of wrapping it or otherwise linking out to it from Go, that is my worst case fallback, but avoiding it. ideal is a 100% pure Go source-to-binary solution)</p><p><a href="https://toot.io/tags/Golang" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Golang</span></a><br><a href="https://toot.io/tags/Python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Python</span></a><br><a href="https://toot.io/tags/sklearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>sklearn</span></a><br><a href="https://toot.io/tags/ScikitLearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ScikitLearn</span></a><br><a href="https://toot.io/tags/ML" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ML</span></a><br><a href="https://toot.io/tags/stats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>stats</span></a><br><a href="https://toot.io/tags/statistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>statistics</span></a><br><a href="https://toot.io/tags/math" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>math</span></a><br><a href="https://toot.io/tags/FOSS" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>FOSS</span></a></p>
Abid<p>Streamline Your Machine Learning Workflow with Scikit-learn Pipelines</p><p>Learn how to enhance the quality of your machine learning code using Scikit-learn Pipeline and ColumnTransformer.</p><p><a href="https://www.kdnuggets.com/streamline-your-machine-learning-workflow-with-scikit-learn-pipelines" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">kdnuggets.com/streamline-your-</span><span class="invisible">machine-learning-workflow-with-scikit-learn-pipelines</span></a></p><p><a href="https://data-folks.masto.host/tags/ScikitLearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ScikitLearn</span></a> <a href="https://data-folks.masto.host/tags/pipeline" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>pipeline</span></a> <a href="https://data-folks.masto.host/tags/python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>python</span></a></p>
PyData Madrid<p>Ya está abierto el registro para nuestra reunión de febrero: 🔍 Eficiencia operacional con LLMs y pipelines de scikit-learn, este mes en las oficinas de Adyen</p><p><a href="https://www.meetup.com/pydata-madrid/events/299189759/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">meetup.com/pydata-madrid/event</span><span class="invisible">s/299189759/</span></a></p><p>¡Nos vemos el jueves 22 a las 19:00! Y después, networking 🍻</p><p><a href="https://masto.ai/tags/PyDataMadrid" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PyDataMadrid</span></a> <a href="https://masto.ai/tags/PyData" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PyData</span></a> <a href="https://masto.ai/tags/python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>python</span></a> <a href="https://masto.ai/tags/ai" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ai</span></a> <a href="https://masto.ai/tags/llm" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>llm</span></a> <a href="https://masto.ai/tags/scikitlearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>scikitlearn</span></a> <a href="https://masto.ai/tags/sklearn" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>sklearn</span></a></p>
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