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Top Machine Learning Tools

by Ahsan Khan

 In today’s digital world, several tasks are done by many kinds of big and small machines and systems. Human workforce and paperwork have been reduced, so here comes the important role of machine learning that gives organizations a view of trends in customer behavior and business operational patterns. It also supports the development of new services and products. It allows users to feed a computer algorithm a massive amount of data and have the computer analyze and make better decisions.  It is providing many benefits to organizations. Therefore it is considered a significant competitive differentiator for many companies. That is why several tech companies, such as Google, Facebook, Uber, etc., have made machine learning a central part of their technological processes.

It is a known fact in today’s time that almost all the companies are running on data which is the lifeblood of all the businesses. Data-driven business decisions can make the difference between keeping up with the tough competition or falling further behind. Here machine learning tools and techniques play a vital role for organizations as they can help unlock the value of corporate and customer data and improve decisions that keep a company ahead of the competition. It resulted in the increased demand for machine learning experts who have  machine learning certificate. This article will focus on some of the important machine learning tools that can be learned easily to achieve these specialized ML certifications.

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What is Machine Learning?

Generally, machine learning is known as a branch of computer science and artificial intelligence that focuses on the use of data and algorithms to imitate or mimic the way that humans can learn by improving their speed and accuracy. It allows software applications and systems to become more accurate at predicting outcomes without being specially programmed to do the tasks. ML tools, techniques, and algorithms use old data as input to predict new output values and trends. It is one of the most exciting technologies that one would have ever used directly or indirectly. It is that intelligent technology that makes computers more similar to humans. It is also known as the field of inquiry that allows ML experts to understand and create methods that learn and can leverage data to improve performance on some specific set of data tasks.

We can understand ML with its few important use cases, such as recommendation engines, spam filtering, fraud detection, malware threat detection, predictive maintenance, and business process automation ( BPA). It is considered one of the essential technologies for data analysts, data scientists, and also for those who want to work with a huge amount of raw data to get insights and trends.

Machine Learning Tools

We use Machine Learning tools in our day-to-day life. There are many types of Machine learning tools available in the market that we can use according to our requirements. Let us know about some of the important ML tools.

  • Scikit-Learn- This ML tool is related to machine learning development in python. It is used for data analysis and data mining. It provides algorithms and models for classification, clustering, regression, model selection, dimensional reduction, and pre-processing. Scikit-learn is available free of cost and provides easily understandable documentation. The main feature of this tool is parameters for any algorithm that can be modified while calling objects.
  • TensorFlow- It is one of the most popular tools/libraries used to train and create a machine learning and deep learning models. It is an open-source ML tool that offers a JS ( Java Script) library popular among machine learning enthusiasts. It is used for creating many different ML applications. Tensorflow provides powerful resources, tools, and libraries for numerical computation in large-scale deep learning and machine learning projects. It is considered a model converter so you can run your existing models. It also helps in the neural networks.
  • PyTorch- is a popular tool in machine learning that helps in creating neural networks through the Autograd Module. It is a Torch-based computing framework, scripting language, and ML library. PyTorch can be used in cloud platforms that can provide optimization algorithms. It also offers several tools, distributed training, and various libraries.
  • Amazon Machine Learning- Amazon is popular worldwide as it offers a huge number of ML tools, one of which is Amazon Machine Learning or AML. It is a robust and cloud-based machine learning application broadly used for creating machine learning models and making accurate predictions. It offers visualizations and wizards that help users to find trends/patterns, make predictions, and create mathematical models. It can integrate data from different sources like Amazon S3,  Redshift, or RDS.
  • Apache Mahout is an open-source project of Apache Software Foundation used for developing ML applications focused on Linear Algebra. It helps developers to use ML techniques such as recommendation, classification, and clustering. It also provides Java/Scala libraries to solve mathematical problems based on statistics and linear algebra.
  • Catalyst- It is also a PyTorch framework specially designed for deep learning solutions. It is a research-friendly tool that can take care of engineering tasks like reproducibility, reusability, and facilitating rapid experimentation. It is an easy-to-use tool that enables developers to operate deep learning models with a few lines of code, as this domain has always been considered a complex one.

Besides these important tools, there are several machine learning tools available that developers use for several different purposes. XGBoost, LightGBM, CatBoost, Fast.ai, PyTorch Ignite, PyTorch Lightning, ApacheSpark MLib, Oryx2, Shogun, NET, Google Cloud ML Engine, etc. are some other ML tools that are commonly used in this drastic domain. But it is clear that there is no one specific tool that can be used for all ML operations. So, developers have to choose according to their requirements and the ML projects they are involved in.

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