This repository contains a Jupyter Notebook that compares the performance of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) architectures for image classification tasks using the Fashion MNIST dataset. The notebook explores the process of training CNN and RNN models

An analysis of potential charity donors using Python Jupyter Notebook. Features cleaning of data, exploration, supervised machine learning and insights.

these are my projects that i submitted for AIML course with great lakes & some good notebooks with great explaination of the topics

This repo houses all my notes, labs and notebooks I used/ created while learning Machine Learning. Following on from this repository I also have a repo for all things Deep Learning too.

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

My notebooks when i was learning Machine Learning with scikit-learn.

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

Notebook used to evaluate various machine learning models used to predict white wine quality.

This repository contains my practice of Introduction to Computer Vision and Image Processing lab notebooks.

Collection of supervised machine learning notebooks

The notebook provides a step-by-step guide to preparing and analyzing geospatial data and creating a target map using supervised ml techniques.

Notebooks con ejercicios y ejemplos del libro Hands on Machine Learning with scikit-learn and tensorflow 2

In this data science course, you will be given clear explanations of machine learning theory combined with practical scenarios and hands-on experience building, validating, and deploying machine learning models. You will learn how to build and derive insights from these models using Python, and Azure Notebooks.

A hub that contains notebooks that implement Regression models, illustrates LR via Gradient Descent, compares K-means vs Spectral vs Hierarchical, compares PCA vs t-SNE

Project using machine learning to predict if water wells in Tanzania are functional, non functional, or in need of repair. Written with python using jupyter notebook for the main project flow/analysis and some visual studio code.

Predicting if a customer will default the next credit card payment using supervised machine learning. Python jupyter notebook attached.