1223 Open source projects that are alternatives of or similar to MachineLearning

No description or website provided.

Can we predict accurately on the skewed data? What are the sampling techniques that can be used. Which models/techniques can be used in this scenario? Find the answers in this code pattern!

All the slides, accompanying code and exercises all stored in this repo. 🎈

Using LSTM network for time series forecasting

Introduction to Statistical Learning with R을 Python으로

A collection of Poisson lognormal models for multivariate count data analysis

Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.

Porn images detector with python, tensorflow, scikit-learn and opencv.

Different clustering approaches applied on different problemsets

A Latent Dirichlet Allocation implementation in Python.

Hands-on in-person workshop for Deep Learning with TensorFlow

A drop-in replacement for Scikit-Learn’s GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques.

🎓 Jupyter notebooks from UFC data science course

A Python Package for Density Ratio Estimation

Credit Card Fraud Detection using ML: IEEE style paper + Jupyter Notebook

Super fast simple k-means implementation for unidimiensional and multidimensional data.

A series of self-correcting challenges for practicing your Machine Learning and Deep Learning skills

⭐ A Python package for optimal 1D k-means clustering.

A Neural Network experiment involving a JavaScript game.

LSTM based human activity recognition using smart phone sensor dataset

Machine Learning and NLP: Text Classification using python, scikit-learn and NLTK

Simple implementation of sar target recognition using machine learning methods

A Reproducible Workflow for Structural and Functional Connectome Ensemble Learning

(Deprecated) Scikit-learn integration package for Apache Spark

Bidirectional Helmholtz Machines

Training time estimation for scikit-learn algorithms

notebooks with example for machine learning examples

Markov Chains and Hidden Markov Models in Python

Transpile trained scikit-learn estimators to C, Java, JavaScript and others.

Project Page of Combining 3D Morphable Models: A Large scale Face-and-Head Model - [CVPR 2019]

Computer vision sabbatical study materials

Matlab library for gradient descent algorithms: Version 1.0.1

Python Client and Toolkit for DataFrames, Big Data, Machine Learning and ETL in Elasticsearch

Predicting forex binary options using time series data and machine learning

R package: parallel computing toolset for relatedness and principal component analysis of SNP data (Development Version)

Alpine Container for Machine Learning

Template codes and examples for Python machine learning concepts

🧙 A web app to generate template code for machine learning

host load prediction with Long Short-Term Memory in cloud computing

A scikit-learn compatible implementation of hyperband

Quick-Data-Science-Experiments

Machine Learning Containers for NVIDIA Jetson and JetPack-L4T

Software platform for clinical neuroimaging studies

Predicting Future Influenza Virus Sequences with Machine Learning

Introduction to Machine Learning with Time Series at PyData Festival Amsterdam 2020

Repository for the ICON 2017 hackathon 'multivoxel pattern analysis (MVPA) of fMRI data in Python'

An Interactive Approach to Understanding Supervised Learning Algorithms

Kaggle Struggle

Parallel random matrix tools and complexity for deep learning

In this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).

MNIST digit classification with scikit-learn and Support Vector Machine (SVM) algorithm.

Using python and scikit-learn to make stock predictions

Comparing FC VAE / FCN VAE / PCA / UMAP on MNIST / FMNIST

📓 deepleaning and cv notes.

Pedestrian Detection using Non Maximum Suppression

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.

Clustering routines for the unit sphere

ML-AI Community | Open Source | Built in Bharat for the World | Data science problem statements and solutions

Python for chemoinformatics

Streaming, Memory-Limited, r-truncated SVD Revisited!