General Assembly's 2015 Data Science course in Washington, DC

Local Interpretable Model-Agnostic Explanations (R port of original Python package)

Machine Learning notebooks for refreshing concepts.

CloudCV GSoC 2020 Ideas

Measure and visualize machine learning model performance without the usual boilerplate.

Rapid Calculation of Model Metrics

An Interactive Approach to Understanding Deep Learning with Keras

🚦Minimal examples of machine learning tests for implementation, behaviour, and performance.

An high-level Scorecard modeling API | 评分卡建模尽在于此

This project analyzes and visualizes the Used Car Prices from the Automobile dataset in order to predict the most probable car price

Flexible tool for bias detection, visualization, and mitigation

Use AutoAI to detect fraud

Titus 2 : Portable Format for Analytics (PFA) implementation for Python 3.4+

Solve complex real-life problems with the simplicity of Keras

skrobot is a Python module for designing, running and tracking Machine Learning experiments / tasks. It is built on top of scikit-learn framework.

🎓 2020 Undergraduate Graduation Project in Jiangnan University ALL codes including Data-convert, keras-Train, model-Evaluate and Web-App

Analyzing the Features which leads to heart diseases and visualizing the models' performance and important features using eli5, shap and pdp.

Classifying badminton strokes based on accelorometer and gyroscope sensor data attached to player's wrist. An end-to-end Machine Learning project, from data collection and preprocessing to final model evaluation.

Easliy get TPR, FPR and KS statistic.

The binary classification problem focused on first IEEE Image forensics challenge-phase 1, to predict the given image is pristine or manipulated/edited/fake. Comparing CNN & Transfer Learning models for the problem and boosting the performance by feature extraction

This course consists of data wrangling, visualization, and decision and model evaluating.

Repository to save projects from udacity's machine learning engineer nanodegree

Personal articles and tutorials for various AI/DL/ML/DS research topics

This Package is developed to evaluate the classification | Regression models based on different metrics.

Udacity Machine Learning Nano degree Program. Project Predicting House prices in Boston

Project 1 for Udacity Machine Learning Nanodegree

Estimated probability models that help target consumers to build brand loyalty

Kaggle Machine Learning Competition Project : In this project, we will create a classifier to classify fashion clothing into 10 categories learned from Fashion MNIST dataset of Zalando's article images

Used linear regression model to train and test the data, evaluated the model performance by calculating the residual sum of squares and the explained variance score (R^2).

In this project we can see in action and in detail a big part of the ML pipeline (data wrangling,model building, model evaluation) that comprises different algorithms and approaches such as Decision Trees (RPART), Linear Discriminant Analysis (LDA), Gradient Boosting Machne (GBM), Random Forest (RF) Support Vector Machine (SVM) with or without Model Stacking, with or without Dimensionality Reduction (with Principal Component Analysis (PCA) or Near-Zero Variance Predictors Filtering)