Top 10 Open Source Machine Learning Libraries

Here is the list of the top 10 open-source machine learning libraries

Machine learning

Machine learning

Machine learning is making wonders across every industry. Open-source machine learning libraries help professionals navigate the complexity of open source code and allow them to get the maximum out of available data. Here are the top open-source libraries for machine learning’s that are popular among data scientists and machine learning engineers.

 

Panda

Panda is one of the best open-source machine learning libraries in the world as it is written in Python Web framework and is used for data manipulation for numerical data and time series. Panda uses data frames and series to define three-dimensional and two-dimensional data respectively and also provides options for indexing large data for quick search in large datasets.

 

Apache Mahout

Apache Mahout is a top Apache Hadoop application that is utilized to make executions of scalable distributed AI calculations, which are centered on the spaces of clustering, collaborative filtering, and classification.

 

SciPy

SciPy is one of the popular open-source machine learning libraries that is loved by all Machine Learning enthusiasts because of the different modules for optimization, linear algebra, integration, and statistics present in it. The SciPy is one of the core packages that make up the SciPy stack and it is also very useful for image manipulation.

 

Scikit-learn

Scikit-learn is widely focused on data modeling concepts such as regression, classification, clustering, model selections, etc., and helps both in supervised as well as unsupervised learnings. The library is written on the top of Numpy, SciPy, and matplotlib. It is one of the open-source machine learning libraries that is commercially usable and also very easy to understand.

 

Microsoft Cognitive Toolkit

Microsoft Cognitive Toolkit, earlier known as Computational Network Toolkit, is an open-source, free, simple to-utilize, and business-grade toolkit that empowers clients to prepare deep learning calculations to learn like the human mind. It portrays neural networks as a progression of computational advances through a guided diagram and permits the clients to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs), and recurrent neural networks.

 

Caffe

Caffe, also known as Convolutional Architecture for Fast Feature Embedding, is a deep learning system made with articulation, speed, and modularity as a top priority. It gives researchers and professionals a spotless and modifiable structure for cutting-edge deep learning calculations and an assortment of reference models. Caffe fits in mechanical and web-scale media needs through broad calculation and handling.

 

Apache Spark MLlib

With famous calculations and utilities, Apache’s Spark MLlib is utilized to perform machine learning in Apache Spark. It expects to make viable machine learning adaptable and simple. The open-source libraries additionally use machine learning calculations like arrangement, relapse, grouping, and community sifting.

 

Featuretools

Featuretools, is a framework widely used for performing automated feature engineering. It excels at transforming temporal and relational datasets into feature matrices for machine learning. Featuretool is an open-source machine learning library that is used to automatically create features from temporal and relational datasets.

 

NLTK

NLTK is the widely used open-source machine learning library for Text Classification and Natural Language Processing. NLTK performs word Stemming, Lemmatizing, Tokenization, and searching a keyword in documents. Along with this, NLKT is also used for sentiment analysis, understanding movie reviews, food reviews, text-classifier, checking, and more.

 

NumPy

NumPy is a very popular Python library for large multi-dimensional array and matrix processing, with the help of a large collection of high-level mathematical functions. It is very useful for fundamental scientific computations in Machine Learning. It is particularly useful for linear algebra, Fourier transform, and more.