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Several leading companies have made machine learning a central part of their operations. Machine learning provides them with a view of trends in customer behavior and operational business patterns. Many upcoming machine learning techniques now support the development of new products and services, hence being an integral part of an organization.

Machine learning technologies are beneficial in many ways. It provides organizations with a view of customer behavior and business patterns and supports the development of new products and services. It also helps save time and money and allows organizations to get things done efficiently and quickly.

Machine learning is gaining more popularity with innovations such as Virtual assistants solutions, driver-less cars, hi-tech robots, face recognition techniques, etc. Therefore there is a high demand for machine learning experts who are certified and skilled in courses such as Machine Learning with R course and other supportive tools, programming languages, etc.

So this article will let you know about some of the Machine learning tools that can help you throughout your career.

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What is Machine Learning?

Machine learning is the most popular part of Artificial Intelligence used in almost every sector like finance, healthcare, self-driving cars, voice assistants, recommendation systems, social platforms, gaming apps, chatbots, infrastructure and marketing, cyber security, etc. In simple language, machine learning is the technical process that allows users to feed a computer algorithm a vast amount of data and have the system analyze and make better data-driven business decisions.

Machine learning is a data analysis method that can automate the creation of analytical models by using a massive amount of data that encompasses diverse forms of digital information, including numbers, images, clicks, and words. It focuses on creating and developing systems that can learn from historical data. Moreover, it find hidden patterns, and make meaningful decisions with little or no human intervention. It involves training a machine to learn more intelligently and more quickly. Also, it can make computers get into self-learning mode without being programmed, which means when we feed new data, these computers let develop, grow, make decisions, and change themselves. It can help organizations reduce process-driven losses, reduce costs by guiding predictive maintenance, and increase the capacity by optimizing the production process.

Top Machine Learning Tools

There are several kinds of machine learning tools available for many different purposes. Following mentioned are some of the top ML tools used by experts.

  • TensorFlow- TensorFlow is a low-level library that requires working with model code. It provides pre-built models that can be used for simple solutions. Also, it offers dataflow graphs that come with ease, especially when complex models are under development. It also involves various solutions such as Predictive ML solutions, computer vision, NLP, and reinforcement learning. This popular tool has over 380,000 contributors worldwide.
  • Scikit-Learn- Scikit learn is a python related machine learning tool. It is a simple and efficient tool used for data analysis and mining. Also, it is an open-source tool that can be used commercially and has a BSD license. It also provides an extensive library for the Python programming language. Scikit learn involves algorithms and models for Regression, Classification, Dimensional reduction, Clustering, Model selection, and Pre-processing. It also provides easily understandable documents. This tool is advantageous as the parameters for any algorithm can be changed while calling objects. This ML tool is available for free.
  • PyTorch is also one of the most popular open-source tools used by Ml experts that gives tough competition to TensorFlow. PyTorch supports many ML tools and libraries that can help with many solutions. It has two main features: Neural networks built on a tape-based auto diff system and Tensor computing with accelerated processing on GPU. As the name shows, PyTorch supports python as well as other programming languages such as C++, Java, etc. It can also help dynamic dataflow graphs where other tools are limited to static graphs. PyTorch is easy to learn and understand involving gaming research platforms like allenNLP and ELF.
  • KNIME- Knime is also[c8onsidered a popular tool in the machine learning field used for data analytics, reporting, and integrating platforms. It can navigate complex data with the agility and freedom that only an open tool can bring. It includes data pipeline concepts and can combine different components for machine learning and data mining. Thi tool is specially used to integrate the codes of programming languages. Such as Javascript, Java, Python, C, C++, and R. KNIME is easy to deploy and install. And can be used in many different sectors. Like business intelligence, CRM, and financial data analysis.
  • Catalyst- This ML tool is based on PyTorch frameworks designed primarily for deep learning solutions. It can perform engineering tasks like code reusability and reproducibility, facilitating rapid experimentation. Catalyst is used in complex fields such as deep learning. Where developers can execute deep learning models with a few lines of code. It also supports top deep learning models like stochastic weight averaging.

Besides these tools, many more advanced and popular tools are available that many Machine learning experts mostly use. These tools are LightGBM, XGBoost, PyTorch Lightning, CatBoost, Fast.ai, PyTorch Ignite, Colab, Weka, Apache Mahout, Accord.Net, Shogun, etc. But it is suggested by many experts that no one tool has to be the solution for every business case. Also to solve any ML problem. So the ideal way is to go for the combination of tools. Since most of them are compatible with each other.