ikatsov /
tensor-house
A collection of reference Jupyter notebooks and demo AI/ML applications for enterprise use cases: marketing, pricing, supply chain, smart manufacturing, and more.
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Suraj-G-Rao / repository
A collection of Machine Learning lab programs implemented in Python (Jupyter Notebook) covering fundamental supervised and unsupervised learning algorithms, search algorithms, and neural network concepts.
A collection of Machine Learning lab programs implemented in Python (Jupyter Notebook) covering fundamental supervised and unsupervised learning algorithms, search algorithms, and neural network concepts.
| Notebook | Description |
|---|---|
Bagging&Boosting.ipynb | Implementation of ensemble learning techniques: Bagging and Boosting |
Bayesian_Network.ipynb | Bayesian Network model implementation |
FOIL.ipynb | First Order Inductive Learner algorithm |
Find_S_Algorithm.ipynb | Candidate Elimination using Find-S concept learning algorithm |
K_means_algo.ipynb | K-Means Clustering implementation |
SOM.ipynb | Self Organizing Map neural network implementation |
candidate_elimination.ipynb | Candidate Elimination algorithm for concept learning |
Through these implementations, I explored:
ML_lab_programs/
│
├── Bagging&Boosting.ipynb
├── Bayesian_Network.ipynb
├── FOIL.ipynb
├── Find_S_Algorithm.ipynb
├── K_means_algo.ipynb
├── SOM.ipynb
├── candidate_elimination.ipynb
└── README.md
Selected from shared topics, language and repository description—not editorial ratings.
ikatsov /
A collection of reference Jupyter notebooks and demo AI/ML applications for enterprise use cases: marketing, pricing, supply chain, smart manufacturing, and more.
HPInc /
📁 This repository hosts a growing collection of AI blueprint projects that run end-to-end using Jupyter notebooks, MLflow deployments, and Streamlit web apps.🛠️ All projects are built using HP AI Studio with ❤️ If you find this useful, please don’t forget to star the repository ⭐ and support our work 🚀
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