✨ A plotter for reinforcement learning (RL)

📉 Visualize your Deep Learning training in static graphics

Usages Examples for ML-Logger

All about machine learning

Machine Learning analysis for an imbalanced dataset. Developed as final project for the course "Machine Learning and Intelligent Systems" at Eurecom, Sophia Antipolis

Implementation of Andrew Ng's machine learning exercises in Julia

Nine diagnostic tools for detecting and understanding overfitting in scikit-learn models — polynomial overfitting, learning curves, validation curves, bias-variance decomposition, regularisation sweeps, data leakage detection, and more. Companion code for the ML Diagnostics Mastery series.

An Implementation of Crawford's and Wright's Learning Curve Production Functions

Built a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.

Using Alexnet Architecture for Multiclass Fruit Classification

Laptop price prediction with regression models, preprocessing, feature engineering, cross-validation, regularization, and learning-curve analysis. Compares linear, polynomial, ridge, lasso, and regression tree models using cross-validated evaluation. (Regression Task ML)

A new package that helps users compare and choose the right data analysis tool by providing structured, expert-level insights. Users input their specific data analysis needs, project requirements, or

Diabetes disease classification with preprocessing, PCA & PSO feature reduction, and SMOTE-based imbalance handling, along with cross-validation, hyperparameter tuning, and learning-curve analysis. Compares logistic regression, Gaussian Naive Bayes, AdaBoost, Random Forest, SVM, MLP, and XGBoost using cross-validated evaluation. (Task 2 ML)

Image processing with python and Machine learning. This is about analyzing Ayurveda plants and detection through uploading an image of plant leaves or flowers.

important code snippets in machine learning

Plotting cost curves of solar PV

Built a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.

Reviewed unstructured data to understand the patterns and natural categories that the data fits into. Used multiple algorithms and both empirically and theoretically compared and contrasted their results. Made predictions about the natural categories of multiple types in a dataset, then checked these predictions against the result of unsupervise…

Learning curve comparison of KNN, Decision Tree, and Linear Regression regressors on an OpenML dataset