An introductory notebook for practical machine learning
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Updated
Jul 13, 2022 - Jupyter Notebook
An introductory notebook for practical machine learning
Competition-winning Rebound Prediction Notebook
This repository contains a collection of fundamental topics and techniques in machine learning. It aims to provide a comprehensive understanding of various aspects of machine learning through simplified notebooks. Each topic is covered in a separate notebook, allowing for easy exploration and learning.
This repo contains notebook with multiple models built for regression.
Exploring a collection of Jupyter notebooks showcasing a variety of Natural Language Processing (NLP) projects.
This notebook presents an exploratory data analysis (EDA) and regression modeling approach for the WiDS Datathon 2024 Challenge #2.
Implementation of selected voting algorithms with a notebook presenting applications. Interestingly, different voting mechanisms produce different results from the same voters' votes.
This notebook uses serval machine tools and techniques to help predict credit risk using data typically seen from peer-to-peer lending services.
Jupyter notebook for the "10-year Coronary Heart Disease (CHD) risk from the Framingham Heart study dataset" project (part of the course in Machine Learning)
This collection contains various projects and notebooks developed to tackle a range of Kaggle competitions, showcasing different machine learning techniques, data preprocessing methods, and model optimizations.
In this notebook, I built gradient boosting classifier and neural network models to classify and predict the survival rate of patients with breast cancer.
Some recent state-of-the-art generative models in ONE notebook: (MIX-)?(GAN|WGAN|BigGAN|MHingeGAN|AMGAN|StyleGAN|StyleGAN2)(\+ADA|\+CR|\+EMA|\+GP|\+R1|\+SA|\+SN)*
Collection and implementation of a variety of machine learning code examples (notebooks and Python scripts) and projects.
A collection of python notebooks that are implementing/using a plethora of machine learning methods.
Jupyter notebook for IoT threat detection using ensemble machine learning. Features data preprocessing, model training (Logistic Regression, Decision Trees, Neural Networks, etc.), and ensemble techniques for enhanced accuracy.
Interactive ML Toolset
Here my amazing tutorial collection contain amazing notebook must read. It's contain pytorch, Advance pandas, Ensemble learning, Tensorflow, Genetic Algorithms, Dask, Word Embedding
A collection of companion Jupyter notebooks for Ensemble Methods for Machine Learning (Manning, 2023)
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