Announcing streamlined MLOps with Orbital on Databricks

Orbital translates #ScikitLearn #Python or #tidymodels #RStats to native #SQL for direct database model execution.

Edgar Ruiz's post uses #Databricks as an integrated environment.

Learn more: https://posit.co/blog/databricks-orbital-r-python-model-deployment/

Lazy-fedi-question... I have a "working"(?) code example of TF-IDF #tfidf using #scikitlearn and I know the main concepts, but all the tutorials I find are a bit — I don't want to be harsh but —crappy... Can someone point me to some nice open resource on it?

I was studying linear regression, and I decided to do a very basic project to consolidate some concepts. And I thought why not put it on GitHub for other people to take a look at. (API that predicts the sale price of a car)

#FastAPI #Python #MachineLearning #ScikitLearn #DataScience #Portfolio #API

https://github.com/GustavoGarciaPereira/projeto_carros_api

GPUs can now accelerate vehicle intrusion detection by up to 159x compared to CPUs.
That’s not a tweak—it’s a leap.

A new study dives into how libraries like cuML outperform scikit-learn in real-time IoV security applications, all while maintaining accuracy.

Could this reshape how we secure connected vehicles at the edge?

Dive into the details: https://blueheadline.com/tech-news/gpu-faster-intrusion-detection-iov/

Discover 10 effective Python one-liners for Scikit-learn that simplify your #MachineLearning 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. #Python #DataScience #SoftwareDevelopment #ScikitLearn #AI https://www.kdnuggets.com/10-python-one-liners-for-scikit-learn

Hi #FediHelp
As I am learning #scikitlearn do you have any resources to work with knn_imputer ?

I want to replace NAN values.

How do you
- Select optimized n_neighbors
- Visualize what the imputer do with plots or metrics

Any link to blog post or tutorials are welcome
Thanks

#python #ai #machinelearning
#mlops #realtime #neuralnetwork #scikitlearn

Continuous Machine Learning (CML): Basics & Best Practices for Adoption
The systematic way of learning continually from real-time data.
Plot: CML Confusion Matrix
#exploremore
https://medium.com/@alexzap922/continuous-machine-learning-cml-basics-best-practices-for-adoption-257c19fbcbe9?sk=98bf072ddaccccd67ba83686261d8b15

#python #algorithm #scikitlearn
#machinelearning #artificialintelligence
#technology
#regression
#guide #tutorial
A Comprehensive Guide to 85 Supervised Machine Learning Algorithms in Scikit-Learn — Part 1. Regressors

Best Practices in Building & Training ML Models with 51 Regressors (Codes, Plots, and More)

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!

#exploremore

https://medium.com/@alexzap922/a-comprehensive-guide-to-85-supervised-machine-learning-ml-algorithms-in-scikit-learn-part-1-9168aee3cf2a?sk=712ecb27162654086ea9780a0d19a5be

For the Francophones: Check out this overview of @swheritage + #Scikitlearn in Le Monde: https://archive.is/BRqAo

Sometimes I wonder how many other people working in some business are learning #python and #scikitlearn because someone said “We need better #forecasting” 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.

I just did my first project using the #mlflow library to track metrics on iterations of manual tuning of an #sklearn pipeline, it works great and gives me some idea of the search space before moving into automated hyperparameter tuning.

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?
#machinelearning #python #MLOps #scikitlearn

https://www.docker.com/blog/supercharging-ai-ml-development-with-jupyterlab-and-docker/ - want to play with #DataScience and #AI? #Jupyter in a #Docker #container is a great way to go. Nice walk-through of #scikitlearn, #matplotlib, and #Python Masahito Zembutsu.

@sirber In the market that I am in, there are almost no postings for #php devs but a heck of a lot for #python. I've been exploring #scikitlearn in particular with python because that seems to be getting hot.

Discover scikit-learn 1.4 and its:
5 major features & 13 features
14 efficiency improvements & 23 enhancements
15 API changes
38 fixes

More details in the changelog: https://bit.ly/3tWlZA3
or in the release highlights: https://bit.ly/3Hsoddm

You can upgrade with pip as usual:
pip install -U scikit-learn

Or using the conda-forge builds:
conda install -c conda-forge scikit-learn

Thanks again to all the +80 contributors!

(1/2) Happy Thanksgiving !

Introduction to Machine Learning with Python and Scikit-Learn Course

FreeCodeCamp released a new machine learning course for beginners with Python and Scikit-Learn yesterday. This 18-hour (holy !) course, by Aakash N S, provides an in-depth introduction to machine learning.

Course : https://www.youtube.com/watch?v=hDKCxebp88A

A follow-up on the decision tree series leading to a random forest this time with details on model building, imbalanced dataset, feature importances & hyperparameter tuning - https://jhylin.github.io/Data_in_life_blog/posts/17_ML2-2_Random_forest/1_random_forest.html

Jupyter notebook link: https://github.com/jhylin/ML2-2_random_forest/blob/main/1_random_forest.ipynb

"Decision tree"

Jennifer HY Lin (@jhylin)

https://jhylin.github.io/Data_in_life_blog/posts/16_ML2-1_Decision_tree/3_model_build.html

"Small molecules in ChEMBL database"

Jennifer HY Lin (@jhylin)

https://jhylin.github.io/Data_in_life_blog/posts/08_ML1-1_Small_molecules_in_ChEMBL_database/ML1-1_chembl_cpds.html

"Small molecules in ChEMBL database"

Jennifer HY Lin (@jhylin)

https://jhylin.github.io/Data_in_life_blog/posts/10_ML1-2_Small_molecules_in_ChEMBL_database/ML1-2_chembl_cpds.html