20+ Free Tools & Resources for Machine Learning
Tags: machinelearning, deeplearning, beginners, tooling
Published: 2022-03-31 21:54:50

## Table of Contents
1. [Open Source Machine Learning Tools for Non-Programmers](#nonprogrammers-ml)
2. [Machine Learning Tools](#machine-learning-tools)
3. [Deep Learning Tools](#deep-learning-tools)
4. [Tools for Reinforcement Learning](#tools-for-reinforcement-learning)

### Non-Programmers (ML)
1. [Uber Ludwig](https://uber.github.io/ludwig/)
Uber's Ludwig is a toolbox built on top of TensorFlow. Ludwig allows you to train and test deep learning models without writing code.

2. [KNIME](https://www.knime.com/)
KNIME lets you create entire data science workflows using a drag and drop interface.

3. [Orange](https://www.analyticsvidhya.com/blog/2017/09/building-machine-learning-model-fun-using-orange/)
Orange lets you mine data, crunch numbers, and derive insights. It has become popular among students and teachers.

### Machine Learning Tools
1. [MLFlow](https://www.analyticsvidhya.com/blog/2018/06/mlflow-an-open-source-machine-learning-platform-that-works-with-any-library-algorithm-and-tool/)
MLFlow is designed to work with any ML library or algorithm and manage the entire life cycle.

2. [TensorFlow.js](https://www.analyticsvidhya.com/blog/2019/06/build-machine-learning-model-in-your-browser-tensorflow-js-deeplearn-js)
This is an open-source library that lets you build and train machine learning models on the web.

3. [Hadoop](https://www.analyticsvidhya.com/blog/2014/11/hadoop-mapreduce/)
4. [Apache Spark](https://www.analyticsvidhya.com/blog/2016/09/comprehensive-introduction-to-apache-spark-rdds-dataframes-using-pyspark/)
5. [Neo4j](https://neo4j.com/)
A graph database management system that allows you to track where private information is stored and which systems, applications, and users access it.

6. [OpenNN](https://www.opennn.net/)
An open-source library, written in C++, that helps in developing deep learning models.

7. [Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning/)
An enterprise-grade service for the end-to-end machine learning life cycle that allows you to build models at scale.

8. [Mahout](https://mahout.apache.org/)
Apache Mahout (TM) is a distributed linear algebra framework and mathematically expressive Scala DSL designed to let mathematicians, statisticians, and data scientists quickly implement their own algorithms. 

9. [Weka 3](https://www.cs.waikato.ac.nz/ml/weka/)
Weka is a collection of machine learning algorithms for data mining tasks.

10. [Compose](https://github.com/FeatureLabs/compose)
Compose targets labeling raw data, allowing you to set labeling functions for your data in Python in order to make the labeling process easier.

11. [Cortex](https://www.cortex.dev/)
Cortex is a cloud infrastructure for machine learning at scale that provides a convenient way to serve predictions using Python, TensorFlow, PyTorch, Scikit-learn, and more.

### Deep Learning Tools
1. [Keras](https://keras.io/)
Keras is an API for neural networks that helps doing quick research.

2. [H2O.ai](https://h2o.ai/)
H2O is a deep learning tool built in Java. It supports most widely used machine learning algorithms and is a fast, scalable machine learning application interface used for deep learning, elastic net, logistic regression, and gradient boosting.

3. [DeepLearningKit](https://github.com/DeepLearningKit/DeepLearningKit)
DeepLearningKit is an open source deep learning tool for Apple's iOS, OS X, tvOS, etc. This tool is developed in Swift.

4. [Theano](https://pypi.org/project/Theano/)
Theano is a Python library that is used to evaluate mathematical operations including multi-dimensional arrays.

5. [lobe.ai](https://www.lobe.ai/)

### Tools for Reinforcement Learning
1. [Unity ML Agents](https://unity3d.com/machine-learning)
2. [Project Malmo](https://www.microsoft.com/en-us/research/project/project-malmo/)
A sophisticated AI experimentation platform built on top of Mincraft.

3. [OpenAI Gym](https://gym.openai.com/)