placeholder_200x200

Top 7 Platforms for Implementing Machine Learning

The everyday tech experiences that are untouched by Machine Learning(ML) grow fewer each day. With its unique capability to help technology solutions grow smarter organically, ML has been crucial in making unprecedented breakthroughs. The use cases of machine learning range from face unlock, dynamic pricing, personalized recommendations, digital power plants, self-evolving fraud detection, to AI for radiology, and much more.

Machine Learning(ML) is the algorithmic application of artificial intelligence that empowers systems with the ability to learn and improve without ample human input. Considered a subset of artificial intelligence, machine learning systems enable rapid and uncomplicated analysis of large and complex data sets. Decisions made on the basis of such analyses tend to be sound and backed by accurate facts.

Since ML allows systems to become more accurate in predicting outcomes, without being specifically programmed, machine learning tools can be used in applications that need growing cognitive power:

  • Building recommendation engines
  • Predicting search patterns
  • Filtering spam
  • Building news feeds
  • Detecting fraud and security threats

 

Machine learning is on the path to becoming a common component of software engineering. As a result, many open-source platforms are making it increasingly possible to implement ML tools with no coding and minimal pre-requisites. In this article, we delve deeper into the popular machine learning tools, especially the ones our experts trust, and discuss what they have to offer.

  1. TensorFlow

It is an open-source framework, which comes in handy for machine learning as well as numerical machine learning. Developed and released by the Google Team, TensorFlow is a blend of machine learning and neural network models. It is flexible and can be used across computation platforms such as CPUs and GPUs as well as the mobile computing platforms. It runs on Mac, Windows, and Linux. Natural Language Processing and image classification use this tool. TensorFlow is one of the must-know tools for machine learning.

Tool Cost: Free

  1. NET

Accord.NET is a machine learning tool, which is combined with audio and image processing libraries written in C#. This framework consists of multiple libraries for a wide array of applications such as pattern recognition, statistical data processing, and linear algebra. This ML tool is used for creating product-grade computer audition, computer vision, signal processing, and statistics apps. It also provides support for graph plotting.

Tool cost: Free

  1. Scikit-Learn

Scikit-Learn is an open-source ML tool in Python. It is a unified platform since it is used for multiple purposes. It assists in regression, clustering, classification, dimension reduction and preprocessing. This software is easily accessible and can be reused in various contexts.

Tool Cost: Free

 

  1. Weka

Weka stands for Waikato Environment for Knowledge Analysis. Written in Java, this tool comprises of several machine learning algorithms which are ready to be deployed. These algorithms are mainly used for data mining. Some key features include data preparation, classification, regression, clustering and visualization. This tool is also flexible for scripting experiments. The best part about this tool is that machine learning can be deployed on existing data without writing any line of code. This makes it ideal for those who are new to ML or coding.

Tool Cost: Free

 

  1. KNIME

KNIME or Konstanz Information Miner is an open-source data analytics reporting as well as an integration platform. It is intuitive and constantly integrates new development features. KNIME assists users in understanding the data and designing the data science workflows using reusable components that are accessible to all. It makes use of a modular data pipelining concept and can integrate the code of programming languages like C, C++, R, Python, Java, and Javascript. KNIME can be used for business intelligence, financial data analysis, and CRM.

Tool Cost: Free

 

  1. PyTorch

PyTorch is an ML tool developed by Facebook’s team along with engineers from Twitter, Salesforce, NRIA, ParisTech, Digital Reasoning, and INRIA in 2016. PyTorch is built on the Torch framework but supports Python. Key features of PyTorch are Deep Neural Networks and Tensors. PyTorch can be used on cloud platforms and its rapid prototyping can be developed for research. It supports dynamic computational graphs, which accelerate machine learning processes. This also proves to be an advantage when one is unsure of how much memory will be required for creating a neural network model. Uber’s probabilistic programming language is built using PyTorch.

Tool Cost: Free

  1. RapidMiner

RapidMiner provides a platform for machine learning, deep learning, data preparation, text mining, and predictive analytics. It can be used for research, education, and application development. It is popular for its lightning-fast speed to drive revenue, reduce costs, and avoid risks. RapidMiner is platform-independent since it works on cross-platform operating systems. With the help of this machine learning tool, one can test their own data as well as test their own models. Its intuitive interface uses the drag-and drop-feature, making it a go-to tool for non-programmers as well.

Tool Cost: RapidMiner offers 4 plans - Free, small, medium, large.

AI and ML are steadily making man-machine interactions more balanced and valuable. At InfoVision, we have leveraged this capability of AI and ML to successfully transform enterprise operations and improve agility, efficiency, and ROI. Supported by an innovation mindset, machine learning tools are capable of creating first-mover advantages in business functions across industries and applications. And one of these seven tools could be the first step in making this happen.