Open-source tools for machine learning helps professionals navigate the complexity of open-source code
Machine learning is making wonders across every industry. Disruptive technology is reshaping the way companies make decisions and deal with ever-growing data. Starting from chatbots to answer customer queries to detecting transaction frauds in banks, machine learning and its applications are streamlining many routine processes. In the past few years, the dominance of machine learning has stepped out of company floors. They are present in our everyday life. As more people and organizations incorporate machine learning into their functionalities, data scientists and machine learning engineers are striving to enhance their capabilities. As a quick remedy, open-source tools help them streamline their machine learning processes. The proliferation of free open-source tools and software has made machine learning easier to implement both on single machines and at scale, and in most popular programming languages. Open-source tools for machine learning helps professionals navigate the complexity of open-source code and allow them to get the maximum out of available data. Therefore, Analytics Insight has listed the top open-source tools for machine learning that are popular among data scientists and machine learning engineers.
Top Open-Source Tools for Machine Learning
Weka, is an open-source tool that deals with data processing activities. It is a collection of machine learning algorithms for data mining tasks. Starting from data pre-processing and implementing machine learning algorithms to visualization of tools and developing machine learning techniques, Weka carries out many data routines. Besides, the tool also helps professionals apply the techniques to real-world data mining problems.
JupyterHub, is an open-source tool that serves Jupyter notebook for multiple users. The Jupyter Notebook is a web application that users can utilize to create and share documents that contain live code, equations, visualizations, and text. JupyterHub is a multi-user hub that can be used in a class of students, a corporate data science group, or a scientific research group to spawn, manage, proxy multiple instances of a single user Jupyter Notebook server.
Apache Mahout, is a top Apache Hadoop application that is used to create implementations of scalable distributed machine learning algorithms, which are focused on the areas of clustering, collaborative filtering, and classification. It is a distributed linear algebra framework and mathematically expressive Scala DSL, designed to let data scientists, mathematicians, and statisticians rapidly implement their own algorithms.
Core ML, is an Apple framework to integrate machine learning models into applications. The open-source tool provides a unified representation for all models. By using Core ML, users can convert models from third-party training libraries such as TensorFlow and PyTorch into a certain application. Applications can use Core ML APIs and user data to make predictions and to fine-tune models, all on the user’s device.
TensorFlow, is an open-source end-to-end platform for creating machine learning applications. It is a symbolic math library that uses dataflow and differentiable programming to perform various tasks focused on the training and inference of deep neural networks. TensorFlow’s library helps train ML models and accelerates them to perform better. Its flexible ecosystem of tools and comprehensive nature pushes researchers to easily build and deploy AM-powered applications.
Microsoft Cognitive Toolkit, formerly known as Computational Network Toolkit, is an open-source, free, easy-to-use, and commercial-grade toolkit that enables users to train deep learning algorithms to learn like the human brain. It describes neural networks as a series of computational steps via a directed graph and allows the users to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs), and recurrent neural networks.
Caffe, also known as Convolutional Architecture for Fast Feature Embedding, is a deep learning framework made with expression, speed, and modularity in mind. It provides scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. Caffe fits in industrial and internet-scale media needs through extensive computation and processing.
Uber’s Ludwig, is a code-free deep learning toolbox that helps made deep learning easier to understand for non-experts and enables faster model improvement iteration cycles for experience machine learning developers and researchers alike. As Uber uses varied model tasks including customer support, object detection, improving maps, streamlining chat communications, forecasting, and preventing frauds, it uses the same technologies to power the Ludwig tool.
With popular algorithms and utilities, Apache’s Spark MLlib is used to perform machine learning in Apache Spark. It aims to make practical machine learning scalable and easy. The open-source tool also leverages ML algorithms such as classification, regression, clustering, and collaborative filtering.
Featuretools, is a framework to perform automated feature engineering. It excels at transforming temporal and relational datasets into feature matrices for machine learning. Featuretool automatically creates features from temporal and relational datasets. It uses Deep Feature Synthesis, Precision Handling of Time, Reusable Feature Primitives, etc. to train machine learning.
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