Welcome to the Machine Learning Repository! This repository is a collection of notebooks showcasing various machine learning projects and implementations. It incluedes Decision tree algorithm, Random forest , Support vector machine etc.
Jupyter notebooks containing explanation of Machine Learning Algorithms
Machine Learning exercises in Python (Jupyter notebooks)
📚 Set of Jupyter Notebooks with different kind of machine learning topics
This Repository consist of some popular Machine Learning Algorithms and their implementation of both theory and code in Jupyter Notebooks
A series of documented Jupyter notebooks implementing SVM and SVC's.
A series of notebooks that introduce Machine Learning concepts with hands-on practice and its mathematics in brief.
Notebooks for ML Tasks w/ Scikit and LLMs using Cohere, HuggingFace, LangChain, and OpenAI
Computer Vision: Vehicle Detection
A series of Jupyter notebooks, to know about Machine Learning, its implementation, and identifying its best practices.
This repository is made following the course by Sir Jose Portilla, and focuses on Supervised Machine Learning algorithms. I studied all these concepts in December 2023
Repository consisting of the notebooks I worked on to submit my courseworks at Royal Holloway
This repository contains introductory notebooks for support vector machine
Support Vector Machine by using python and jupyter notebookes , its a Face Detection System
These notebooks were created for the course MME 26849: "Fundamentals of Deep Learning and TinyML"
This repository contains my collections of labs' notebooks from Udacity's Intro to ML with TensorFlow.
Experimental notebooks on blink detection problem by analizing it with simple thresholds, timeseries approach and a ml model.
Master Decision Trees & Ensembles in Python with this ML notebook! Classification, Regression, Bagging, Boosting, & Tuning. Elevate your ML skills now! 🌲🚀
Built from scratch SVM, Kernel Perceptron and Neural Network implemented to recognize handwritten digits from the mnist dataset. Includes jupyter notebook of code, mnist handwritten digit data and a PDF of the code & results.
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