#Learning-materials
Showing 8 of 8 repositories tagged #learning-materials, ranked by stars
Awesome Learning - Learn JavaScript and Front-End Fundamentals at your own pace
📊 A practical toolkit for feature engineering and selection in Python. Explore EDA, handle missing data and outliers, scale/encode/transform features, discretize, and select via filter, wrapper, embedded, shuffling, and hybrid methods. Comes with datasets, clean notebooks, reusable utils, and clear visuals for fast, reproducible ML. Workflows.
🩺 Advanced neural network for breast cancer classification using Wisconsin dataset. Analyzes cell nucleus characteristics from FNA samples to distinguish malignant/benign masses with 96.5% accuracy. Features comprehensive documentation, automated setup, testing framework, and deployment guides. Educational ML project with 15,000+ lines of docs.
🪨 Machine learning project using logistic regression to classify sonar signals as either rocks or mines. Uses scikit-learn to train a binary classifier on sonar dataset with 60 numerical features for accurate underwater object detection.
🔬 Reproducible sandbox for Gaussian Naive Bayes (GNB) applied to cancer cell classification — includes an interactive notebook, data layout and preprocessing guidance, feature-extraction tips, a lightweight scikit-learn pipeline, evaluation protocols for small/imbalanced biomedical datasets, and example scripts for prepare/train/evaluate.
🦠 Covid19 Visualization & Analysis is a community-driven Python project for exploring, visualizing, and analyzing global Covid-19 data. It features interactive notebooks, scripts, and guides to help users uncover trends, create impactful visualizations, and share insights for research, education, and public awareness.
🩺 Machine Learning diabetes prediction model using Support Vector Machine (SVM) classifier. Analyzes 8 medical features (glucose, BMI, age, etc.) from Pima Indian dataset to predict diabetes risk with 75-80% accuracy. Built with Python, scikit-learn, pandas. Includes data preprocessing, model training, and prediction system for diabetes..
📘 This repository offers a complete K-Nearest Neighbors (KNN) tutorial, guiding you from core theory to hands-on practice. Learn to implement KNN from scratch with NumPy, apply it using scikit-learn, and explore visualizations, datasets, and Jupyter notebooks to fully understand, test, and optimize the algorithm.