How to Train Machine Learning model withou ML Library with simple Python code a internal work ? then follow below link - it has video also

https://amitdhanani.in/2025/02/15/how-to-train-machine-learning-model-withou-ml-library-with-simple-python-code-a-internal-work/

#machinelearning #python #linearregression #ml #mlwithoutlibrary #pythonml #supervisedLearning

What is Machine Learning (ML)? Definition, Its Type, and How Does It Work?

See here - https://techchilli.com/artificial-intelligence/what-is-machine-learning-ml-definition-its-type-and-how-does-it-work/

#MachineLearning #AI #Technology #DataScience #Innovation #SupervisedLearning #UnsupervisedLearning #ReinforcementLearning #TechExplained #FutureOfTech

In this blog post, I showcase the power of {tidymodels} and tidy coding principles for efficiently prototyping and fine-tuning different models at the same time 💪

#rstats #tidymodels #classification #machinelearning #supervisedlearning

https://blog.tidy-intelligence.com/posts/classification-customer-churn/index.html

Dear Machine Learning + REversing people, one question: I'm working on a model to find better matches in #Diaphora. It's implemented in this way:

- 1st, I find good matches in the 2 binaries being compared with heuristics proven to be working.
- Using these good (and also bad) matches, I build a dataset for the current 2 binaries, and I train a classifier.

The model is later on used to predict if future matches are good or bad.

#MachineLearning #SupervisedLearning #ReverseEngineering

📢 Vortragshinweis:

Im Rahmen des #DigitalClassicist seminar halten Torsten Hiltmann & Martin Dröge morgen einen Vortrag zum Thema: Geschichte aus der Maschine - Funktionsweise, Möglichkeiten und Grenzen von #MachineLearning für die geschichtswissenschaftliche Forschung und Lehre

Dabei wird es u.a. um #SupervisedLearning, #LLM & die Relevanz von Methodenkritik gehen.

🗓️ 31. Oktober, 16 Uhr c.t.
🌐 Teilnahme möglich online via Zoom: https://digiclass.bbaw.de/seminar.html

_____
#DigitalHistory @histodons

Random forest and tree methods are OP.

Less sensitive to class imbalance and unnormalised features

Can get feature importances.

Easy to use.

My go-to.

#datascience #programming #supervisedlearning