There are several Machine Learning tools and techniques but choosing the right tool is really an important tasks for the practitioner and researchers. In this blog you will learn about most popular and widely used Machine learning tools.
It is used in two ways such as using script tags or by using NPM. It is difficult to learn.
It is an open source framework for development of machine learning in python. It provides models and algorithms for regression, classification, clustering, preprocessing, dimensionality reduction and model selection. It is built on NumPy, SciPy and Matplotlib.
Easily understandable documentation is available. It helps in training and testing your models.
It is an open-source deep learning framework and is based on a python ML Library i.e Torch. It includes tensor calculations and building deep neural networks. It provides several optimization algorithms for deep learning. It is also used in the cloud platform.
It is useful for creating computational graphs. It has a hybrid front-end hence, it is easy to use.
It is an open-source platform based on GUI for data analytics. It uses a pipelining concept and combines different nodes for data mining and machine learning and create a complete workflow. It can integrate other programming languages such as C++, python, java R etc.
It is easy to learn and easy to deploy but it has limited visualization.
Colab is a cloud service provided by Google. It supports python and helps developing several Machine Learning models and applications using several libraries such as Pytorch, Open Cv, Tensor flow etc. It can be used from google drive.
It is an open-source platform to develop several models and algorithms for Regression, Clustering, pre-processing, Recommenders, and Distributed Linear Algebra. It follows a Distributed linear algebra framework. It works for large datasets but some algorithms are missing in this framework.
Keras is an API for neural networks. It supports convolutional and recurrent neural networks. It can run GPU and CPU. It is user friendly and Extensible. Fast prototyping is possible with the help of the library’s high-level, understandable interface, the division of networks into sequences of separate modules that are easy to create and add.
In order to use Keras, you must need TensorFlow, Theano, or CNTK
Rapid Miner provides a platform for machine learning, deep learning, data preparation, text mining, and predictive analytics. Through GUI, it helps in designing and implementing analytical workflows. It helps in data preparation. It is extensible through plugins. It is easy to use but this tool is costly.
It is open-source software and helps in data mining. Weka lets you access other machine learning tools as well. For example, R, Scikit-learn, etc. It supports features such as Data preparation, Classification, Regression, Clustering, Visualization and Association rules mining.
It is Easy to understand algorithms. But not much online support is available.
It is an open source, free machine learning library, provides various algorithms and data structures for machine learning. It also supports many languages like R, Python, Java, Octave, C#, Ruby, Lua, etc. It mainly focuses on kernel machines like regression problems and support vector machines for classification. It can process a large amount of data such as 10 million samples
Jupyter notebook is one of the most widely used machine learning tools among all. It is a very fast processing as well as an efficient platform. Moreover, it supports three languages viz. Julia, R, Python.
Thus the name of Jupyter is formed by the combination of these three programming languages. Jupyter Notebook allows the user to store and share the live code in the form of notebooks. One can also access it through a GUI. For example, winpython navigator, anaconda navigator, etc.
12.Azure Machine Learning Studio
Amazon Machine Learning (AML) is a cloud-based and robust machine learning software. It supports three types of models, i.e., multi-class classification, binary classification, and regression. Fundamental concepts of Azure ML studio are ML models, Data sources, Evaluations, Real-time predictions and Batch predictions.