Best Tools to develop Machine Learning Projects

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Best Tools to develop Machine Learning Projects

By ridhigrg |Email | Jul 16, 2019 | 11067 Views

For developing the system with the required training data to erase the drawbacks and make the machine or device intelligent. Only, a well-defined software can build up a fruitful machine. However, nowadays we develop our machine such a way that we no need to give any instruction about the surroundings. The machine can act by itself, and also it can understand the environment. Therefore, we no need to guide him. As an instance, a self-driving car. Why is a machine so dynamic at present? It's only for developing the system by utilizing machine learning tools.

Best Machine Learning Software and Tools

Best machine learning software without having the software, the computer is an empty box as it is unable to perform its given task. Just like that also a human is helpless to develop a system. However, to develop a machine learning project there is several software or tools are available.

1. Google Cloud ML Engine

If you are training your classifier on thousands of data, your laptop or PC might work well. However, if you have millions of training data? Or, your algorithm is sophisticated and take a long time to execute? To rescue you from these, Google Cloud ML Engine comes. It's a hosted platform where developers and data scientists develop and run high-quality machine learning models.

Features:

Provides ML model building, training, predictive modeling, and deep learning.
The two services namely training and prediction can be used jointly or independently.
This software is used by the enterprises, i.e., detecting clouds in a satellite image, responding faster to customer emails.
It can be used to train a complex model.

2. Amazon Machine Learning (AML)

Amazon Machine Learning (AML) is a robust and cloud-based machine learning software which can be used by all skill levels of developers. This managed service is used for building machine learning models and generating predictions. It integrates data from multiple sources: Amazon S3, Redshift or RDS.

Features:

Amazon Machine Learning provides visualization tools and wizards.
Supports three types of models, i.e., binary classification, multi-class classification, and regression.
Permits users to create a data source object from the MySQL database.

Also, it permits users to create a data source object from data stored in Amazon Redshift.

Fundamental concepts are Data sources, ML models, Evaluations, Batch predictions, and Real-time predictions.

3. Accord.NET

The Accord.Net is a .Net machine learning framework combined with audio and image processing libraries written in C#. It consists of multiple libraries for a wide range of applications, i.e., statistical data processing, pattern recognition, and linear algebra. It includes the Accord. Math, Accord.Statistics, and Accord.MachineLearning.

Features:

Used for developing production-grade computer vision, computer audition, signal processing, and statistics applications.
Consists of more than 40 parametric and non-parametric estimation of statistical distributions.
Contains more than 35 hypothesis tests including one way and two-way ANOVA tests, non-parametric tests like Kolmogorov-Smirnov test and many more.
It has more than 38 kernel functions.

4. Apache Mahout

Apache Mahout is a distributed linear algebra framework and mathematically expressive Scala DSL. It is a free and open source project of the Apache Software Foundation. The goal of this framework is to implement an algorithm quickly for data scientists, mathematicians, statisticians.

Features:

An extensible framework for building scalable algorithms.
Implementing machine learning techniques including clustering, recommendation, and classification.
It includes matrix and vector libraries.
Run on the top of Apache Hadoop using the MapReduce paradigm.

5. Shogun

An open source machine learning library, Shogun, was first developed by Soeren Sonnenburg and Gunnar Raetsch in 1999. This tool is written in C++. Literally, it provides data structures and algorithms for machine learning problems. It supports many languages like Python, R, Octave, Java, C#, Ruby, Lua, etc.

Features:

This tool is designed for large scale learning.
Mainly, it focuses on kernel machines like support vector machines for classification and regression problem.
Allows linking to other machine learning libraries like LibSVM, LibLinear, SVMLight, LibOCAS, etc.
It provides interfaces for Python, Lua, Octave, Java, C#, Ruby, MatLab, and R.
It can process a vast amount of data like 10 million samples.

Source: HOB

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