9 Most Popular Machine Learning Tools You Should Know in 2023

Machine Learning is one of the prominent ways by which companies can remould the way they utilize big data to understand their customer’s demands, their behavioral patterns.With the consistent generation of data and its usage, it has become imperative to build machine learning models. Machine learning tools are algorithmic applications of AI that allow systems to learn and improve without human support. With its growing reach, companies today are trying out a number of machine learning tools. 

Thus, if you want to increase your chances of getting hired, you need to be proficient in handling these machine learning tools. In this article, we will talk about 9 most popular machine learning tools that are a great choice for beginners, enthusiasts and experienced professionals.

In this list we have tried to collate the top and most in-demand tools of 2021. While there are basic level tools like KNIME, we also have the popular tool Shogun. Most AI/ML tools are based around predictive modelling and data. In this article we have tried to narrow down their specific applications and benefits. Some of the mentioned tools like KNIME, Apache Mahout and Weka are open source which means you can start learning them right now!


KNIME is an open-source machine learning tool for data analytics, business intelligence, and text mining. It can be used in finance, pharmaceuticals, and CRM. It is one of the most easy to learn and install machine learning tools. The best part about KNIME is that it can integrate codes of programming languages like Python, Java, R, JavaScript, C++, etc. If  you are a beginner in the field of AI and machine learning, you should definitely try your hands on this tool as its platform has been built for powerful analytics on a Graphical User Interface workflow. This implies that even if you do not have knowledge of coding, you’ll be able to derive insights using KNIME. Taking up KNIME Courses will help you learn the concept comprehensively.

2. Weka

Weka is an open-source machine learning tool that helps in data classification, preparation, regression, clustering, visualization, and data mining. Written in Java, it supports platforms like Linux, Mac OS, Windows. It comprises a collection of algorithms for data analysis, predictive modeling, and data visualization.Thanks to its easy to understand algorithms, it is widely used for teaching and research purposes and also for industrial applications. 

3. BigML

With its goal to make machine learning easy, simple, and beautiful for all users, BigML is one of the most comprehensive machine learning tools. It offers a managed platform to create and share your datasets and models. It is a highly scalable, cloud based, easy to integrate and use tool. BigML is loaded with a wide range of machine learning features such as regression, classification, cluster analysis, topic modeling, anomaly detection, etc.

4. Colab

Colab is a Google Research product for performing machine learning tasks. It helps in building machine learning applications using libraries of Keras, TensorFlow, and OpenCV. It is a free resource which has cloud storage. This means that when you create a Colab notebook, it will be automatically saved in your Google Drive account. This feature of Colab makes it easy to share work with peers.

5. Amazon Machine Learning

It is a robust, cloud-based tool that simplifies the process for developers of all skill levels to use machine learning. You can create powerful models using Amazon ML. It provides visualization tools, wizards as well as supports 3 types of models: binary classification, multi-class classification, and regression. Amazon ML is powerful enough to handle large datasets using multiple servers. Furthermore, customizations are easy to modify and adjust.

6. Apache Mahout

Apache Mahout is an open source machine learning tool built using Java Scala. It is cross-platform friendly and provides algorithms for regression, clustering, recommenders and distributed linear algebra. It is simple, extensible and can be used for large datasets. It is primarily used by data scientists, statisticians, and mathematicians to implement ML algorithms speedily.

7. IBM Watson Studio

IBM Watson Studio helps users to build, run and manage their machine learning models. It brings all the open-source tools like RStudio, Spark and Python together in an integrated environment. It also provides additional tools such as Spark service and data shaping facilities. In this way IBM Watson Studio provides you with all the tools that you need to solve business problems. It offers a drag and drop data prep facility, along with blending and modeling, text analytics for unstructured data and a well-documented, easy to use API.

8. Shogun

Shogun is a machine learning tool that provides various algorithms and data structures to execute your project. A free machine learning tool, Shogun is programmed in C++ and is supported by Windows, Linux, UNIX, and Mac OS. It also offers support for many languages likes R, Ruby, Python, Scala, Lua, Java, and Octave. It is an easy to use machine learning tool that offers good features and functionalities that can process large datasets. It provides support to vector machines, online learning, dimensionality reduction, clustering, regression, and classification.

9. Spell

Spell is a machine learning tool that specializes in building and managing machine learning projects in dynamic environments. Users can distribute their code to run projects in parallel, access collaborative Jupyter workspace and resources, and deploy models in Kubernotes-based infrastructure. With an excellent command-line interface and usability that supports simple commands, it takes care of the infrastructure to quick start projects.


These were the top 9 machine learning tools that we think you should know in 2021. In this list we have tried to collate the top and most in-demand tools of 2021. While there are basic level tools like KNIME, we also have the popular tool Shogun. Most AI/ML tools are based around predictive modelling and data.

How many of these tools did you know about?

You too can learn to work around these tools and in turn develop your own models that will make you an expert. As the influence of machine learning is growing, it has become important to be well-equipped with AI and machine learning, in order to land a job in this fast-growing domain.

The Post Graduate Program in Artificial Intelligence & Machine Learning: Business Applications by McCombs School of Business at The University of Texas at Austin offers the right blend of conceptual and practical knowledge needed to power ahead in this field. 

Any tool that should have featured on this list? Comment below and let us know.

Source : https://www.mygreatlearning.com/blog/artificial-intelligence/