21 Must-Know Open Source Tools for Machine Learning you Probably Aren’t Using (but should!)

It contains tools that can be used in a pipeline to Convert a string containing human language text into lists of sentences and words Generate base forms of those words, their parts of speech and morphological features, and Give a syntactic structure dependency parse BERT as a Service: All of you NLP enthusiasts would have already heard about BERT, the groundbreaking NLP architecture from Google, yet you probably haven’t come across this very useful project.

Bert-as-a-service uses BERT as a sentence encoder and hosts it as a service via ZeroMQ, allowing you to map sentences into fixed-length representations in just two lines of code.

Google Magenta: This library provides utilities for manipulating source data (primarily music and images), using this data to train machine learning models, and finally generating new content from these models.

LibROSA: LibROSA is a Python package for music and audio analysis.

It provides the building blocks necessary to create music information retrieval systems.

It is used a lot in audio signal preprocessing when we are working on applications like speech-to-text using deep learning, etc.

  Open Source Tools for Reinforcement Learning RL is the new talk of the town when it comes to Machine Learning.

The goal of reinforcement learning (RL) is to train smart agents that can interact with their environment and solve complex tasks, with real-world applications towards robotics, self-driving cars, and more.

The rapid progress in this field has been fueled by making agents play games such as the iconic Atari console games, the ancient game of Go, or professionally played video games like Dota 2 or Starcraft 2, all of which provide challenging environments where new algorithms and ideas can be quickly tested in a safe and reproducible manner.

  Here are some of the most useful training environments for RL: Google Research Football:  Google Research Football Environment is a novel RL environment where agents aim to master the world’s most popular sport—football.

This environment gives you a great amount of control to train your RL agents, watch the video to know more: OpenAI Gym: Gym is a toolkit for developing and comparing reinforcement learning algorithms.

It supports teaching agents everything from walking to playing games like Pong or Pinball.

In the below gif you see an agent which is learning to walk.

Unity ML Agents: The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source Unity plugin that enables games and simulations to serve as environments for training intelligent agents.

Agents can be trained using reinforcement learning, imitation learning, neuroevolution, or other machine learning methods through a simple-to-use Python API.

Project Malmo: The Malmo platform is a sophisticated AI experimentation platform built on top of Minecraft, and designed to support fundamental research in artificial intelligence.

It is developed by Microsoft.

  End Notes As it must have been evident by the above set of tools that open source is the way to go when we consider data science and AI-related projects.

I have probably just scratched the tip of the iceberg but there are numerous tools available for a variety of tasks that make life easier for you as a data scientist, you just need to know where to look.

In this article, we have covered 5 interesting areas of data science that no one really talks much about ML without code, ML deployment, Big data,  Vision/NLP/Sound and Reinforcement learning.

These 5 areas, I personally feel have the most impact when the real-world value of AI is taken into account.

What are the tools that you think should have been on this list?.Write your favorites below for the community to know!.You can also read this article on Analytics Vidhyas Android APP Share this:Click to share on LinkedIn (Opens in new window)Click to share on Facebook (Opens in new window)Click to share on Twitter (Opens in new window)Click to share on Pocket (Opens in new window)Click to share on Reddit (Opens in new window) Related Articles (adsbygoogle = window.

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