416 Open source projects that are alternatives of or similar to fastknn

There are plenty of courses and tutorials that can help you learn machine learning from scratch but here in GitHub, I want to solve some Kaggle competitions as a comprehensive workflow with python packages. After reading, you can use this workflow to solve other real problems and use it as a template.

This is a comprehensive ML techniques with python: Define the Problem- Specify Inputs & Outputs- Data Collection- Exploratory data analysis -Data Preprocessing- Model Design- Training- Evaluation

Top 5th percentile solution to the Kaggle knowledge problem - Bike Sharing Demand

A collection of non-residential buildings for performance analysis and algorithm benchmarking

使用sklearn做特征工程

Comprehensive toolkit for generating various numerical features of protein sequences

Paraphrase question identification using Feature Fusion Network (FFN).

A curated list of resources dedicated to Feature Engineering Techniques for Machine Learning

A miniature lisp-like language for querying JSON-like structures. Tuned for clientside ML feature extraction.

This repository contains the code related to Natural Language Processing using python scripting language. All the codes are related to my book entitled "Python Natural Language Processing"

An intuitive library to extract features from time series

Code for Kaggle Data Science Competitions

📢 Ready to learn or review your knowledge!

A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.

Feature engineering package with sklearn like functionality

Stock for Deep Learning and Machine Learning

Open solution to the Home Credit Default Risk challenge 🏡

Kaggle：Quora Question Pairs, 4th/3396 (https://www.kaggle.com/c/quora-question-pairs)

Automatic feature engineering using Generative Adversarial Networks using TensorFlow.

Data transformations for the ML era

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

This repository consist of a 50-day program. All the statistics required for the complete understanding of data science will be uploaded in this repository.

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.

Code for Kaggle and Offline Competitions

LAMA - automatic model creation framework

Automatic feature engineering using deep learning and Bayesian inference using TensorFlow.

Use advanced feature engineering strategies and select best features from your data set with a single line of code.

Features selector based on the self selected-algorithm, loss function and validation method

Using Kaggle Data and Real World Data for Data Science and prediction in Python, R, Excel, Power BI, and Tableau.

DeltaPy - Tabular Data Augmentation (by @firmai)

Default risk prediction for Home Credit competition - Fast, scalable and maintainable SQL-based feature engineering pipeline

Flexible time series feature extraction & processing

Material of the Kaggle Berlin meetup group!

Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.

AntroPy: entropy and complexity of (EEG) time-series in Python

Kaggle을 처음 접하는 사람들을 위한 문서

Official code for "Mean Shift for Self-Supervised Learning"

An experimental Python package that reimplements AutoGBT using LightGBM and Optuna.

🏆 A Comparative Study on Handwritten Digits Recognition using Classifiers like K-Nearest Neighbours (K-NN), Multiclass Perceptron/Artificial Neural Network (ANN) and Support Vector Machine (SVM) discussing the pros and cons of each algorithm and providing the comparison results in terms of accuracy and efficiecy of each algorithm.

A basic framework for pulmonary nodule detection and characterization in CT

Colab Compatible FastAI notebooks for NLP and Computer Vision Datasets

Kaggle solutions

Solution of the Titanic Kaggle competition

R wrappers to connect Python dimensional reduction tools and single cell data objects (Seurat, SingleCellExperiment, etc...)

A experiment to visualize Tree in NewYork and Flight record data. Using Deck.gl and Kaggle

A schema-aware Scala library for data transformation

ml & dl & kaggle

Master's degree project: Development of a trading algorithm which uses supervised machine learning classification techniques to generate buy/sell signals

🔥 Fast, simple sklearn-like feature processing for Go

Simple solution for Open Images 2019 - Instance Segmentation competition using maskrcnn-benchmark.

Compare neural networks by their feature similarity

kNN-based next-basket recommendation

Feature extraction of speech signal is the initial stage of any speech recognition system.

Personalized Medicine: Redefining Cancer Treatment with deep learning

Kaggle Titanic example

🎒 Bag of Visual words (BoW) approach for object classification and detection in images together with SIFT feature extractor and SVM classifier.

Kuzushiji Recognition Kaggle 2019. Build a DL model to transcribe ancient Kuzushiji into contemporary Japanese characters. Opening the door to a thousand years of Japanese culture.

C++ implementation of the nonrigid descriptor Geobit presented at ICCV 2019 "GEOBIT: A Geodesic-Based Binary Descriptor Invariant to Non-Rigid Deformations for RGB-D Images"

This repo provides projects on deep-learning mainly using Tensorflow 2.0

Framework to build your own reverse image search engine