Learn how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python! This repository contains code and datasets used in my book, "Text Analytics with Python" published by Apress/Springer.

My blogs and code for machine learning. http://cnblogs.com/pinard

Machine Learning Model for Sport Predictions (Football, Basketball, Baseball, Hockey, Soccer & Tennis)

Compilation of R and Python programming codes on the Data Professor YouTube channel.

Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.

Fast and memory-efficient clustering

A centralized repository to report scikit-learn model performance across a variety of parameter settings and data sets.

An experiment about re-implementing supervised learning models based on shallow neural network approaches (e.g. fastText) with some additional exclusive features and nice API. Written in Python and fully compatible with Scikit-learn.

A binary release of trained deep reinforcement learning models trained in the Atari machine learning benchmark, and a software release that enables easy visualization and analysis of models, and comparison across training algorithms.

a sklearn wrapper for Google's BERT model

Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code.

MTCNN with Motion Detection, on Raspberry Pi with Love

Virgilio is developed and maintained by these awesome people. You can email us virgilio.datascience (at) gmail.com or join the Discord chat.

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

Machine Learning Tutorials in Python

A Case Study Approach to Successful Data Science Projects Using Python, Pandas, and Scikit-Learn

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

Text Classification through CNN, RNN & HAN using Keras

A library that incorporates state-of-the-art explainers for text-based machine learning models and visualizes the result with a built-in dashboard.

Kaggle Struggle