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Top 8 Machine Learning Tools You Should Know in 2023

By Manas Kochar Category Machine Learning Reading time 10-12 mins Published on Mar 01, 2023

The Best Tools for Machine Learning Enthusiasts Out There

Machine learning consists of evaluating the data, learning from it, and drawing conclusions. Although this seems quite easy, it's ultimately not. Dealing with complex machine learning algorithms is nothing but a matter of expertise. However, there are several machine-learning tools out there that make the use of advanced ML techniques quite easy.

Practitioners find it convenient to analyze and examine data when the process does not involve writing the same code repeatedly each time you create a model.

We are now getting surrounded by different kinds of automation and AI devices. Alexa, Google devices at home, as well as automated chatbot services everywhere are the biggest examples of the same.

Every smart device we use in daily life is continually becoming smarter. However, all of this is because of the stunning advancement of several ,machine learning tools and techniques.

But as its applications spread into automating most of our daily tasks, ML experts need to regularly update themselves on the latest tools and technologies in machine learning.

If you also belong to the emerging field of machine learning, mastering the trending machine learning tools can help you build better models to train new algorithms. You can bring efficiency to your models by applying proper ML tools in the right places.

This is a complicated task, which is why knowledge of different machine-learning techniques is essential. You need to know which tools and software are evolving, modified, or changed. This will help you choose the right set of tools for machine learning.

Selecting the best tool for a task is tough. However, the below-mentioned set of tools will help you a lot.

1. TensorFlow

Developed by the Google Brain Team, this open-source library helps train machine learning and deep learning algorithms. It provides a JavaScript library for large-scale machine learning and numerical computations. The Keras API provides a high-level interface for building machine learning models quickly.

TensorFlow runs on both CPU and GPU. A wide range of applications users use TensorFlow, a few of which are

  • NLP (Natural Language Processing) in virtual assistance systems

  • Image classification

  • Computer vision

  • Reinforcement learning


  • It offers various pre-built models used in solving minor problems.

  • Easy model deployment, inbuilt visualization tools, complex numeric calculations, etc.

  • The most appealing aspect is the use of data flow graphs for complex model development.

  • It also helps build neural networks.

  • It is highly flexible and free to use.

2. Amazon Machine Learning (AML)

This is one of the best machine-learning tools provided by Amazon on its platform. AML is cloud-based and offers reliable machine-learning software. Its primary usage is making ML models and predictions. It provides a simplified tool for developers to utilize machine learning with ease.


  • It provides multiple servers to handle big datasets.

  • Developers can create powerful models using AML.

  • Amazon S3, Redshift, or RDS data sources are available.

  • It offers visualization tools and wizards.

  • AML supports multi-class classification, binary classification, and linear regression models.

  • It includes core ML concepts such as batch predictions, real-time predictions, evaluations, and data sources.

  • Users can retrieve predictions using batch or real-time APIs.

An image of a humanoid brain mounted on an electronic circuit board.

3. Shogun

This tool is programmed in C++ and supports other languages such as Python, R, Scala, and Ruby, among others. Shogun attempts to solve problems such as classification and regression by employing the random forest algorithm for kernel-based algorithms like K-means clustering, support vector machine (SVM), etc.

Its main applications are clustering, online learning, dimensionality reduction, classification, regression, and SVM. The ability to process large datasets is the main feature of Shogun.


  • It can support pre-calculated kernels.

  • Using the multiple kernel learning functionality, developers can use a combined kernel.

  • It can process large datasets with up to 10 million samples.

4. IBM Watson

IBM Watson is one of the essential machine learning tools that help users solve business problems. Data shaping services and tools like Spark also help build and run machine learning models. In addition, open-source tools such as Spark, RStudio, and Python are brought together in an integrated environment.


  • It is an IBM cloud service helpful in developing deep learning and ML models.

  • Primarily useful in using API connections to build machine learning applications successfully.

  • It can perform the ML operations like testing, researching, and training data.

  • It is an NLP-based system, and fields like automated learning, information retrieval, etc., employ Watson.

  • It has an easy-to-use API.

5. Google Colab

Sometimes known as Collaboratory, it is among the most efficient machine learning tools in the market. Colab helps users use Keras, OpenCV, TensorFlow libraries, etc., to build ML models. You store everything in Google Drive and access it whenever since it is based on cloud service.


  • Built on top of the Jupyter Notebook, it provides the same interfaces and utilities.

  • It has cloud storage access, making it easy to work and share with your network.

  • It offers pre-installed libraries.

  • Users get free access to powerful GPUs and TPUs to run advanced applications.

  • It can support all major Python versions.

An image titled, 'PyTorch', with its logo in the center.

6. PyTorch

It is an open-source ML framework developed by Facebook's AI research lab. It uses a strong GPU to offer quickness and flexibility in its functions. Still a newly developed ML tool, PyTorch already has major applications in building deep neural networks and tensor calculations.

It is completely Python-based and uses a library based on Torch. Various technologies use PyTorch, such as

  • NLP (RNN, LSTM, sentiment analysis)

  • Computer vision (object detection and image recognition)

  • Reinforcement learning.


  • It offers flexibility and speed to deep learning tools.

  • It has different machine-learning tools and libraries.

  • Users can use it on cloud platforms as well.

  • Randomly changing the network behavior won't produce lag.

7. Weka

This is one of the open-source machine learning tools that are built on Java and can run on Mac OS, Linux, and Windows. Weka has utilizations in industrial applications and also for teaching and research projects. It provides help in data mining, preparation, regression, visualization, clustering, and classification.

It includes tools for data preprocessing, ML algorithms implementation, and visualization tools, which assist in developing machine learning models for real-world solutions.


  • Different algorithms for data visualization, predictive modeling, and data analysis are easily available.

  • It also provides access to machine-learning tools like Scikit-learn, R, etc.

  • It has an easy-to-use GUI that does not require users to know programming to complete a machine-learning task.

  • The cmd (command line) provides access to each software's capabilities. It is helpful while writing large processes.

  • The tool has Java APIs installed.

8. Knime

Knime is a GUI-based open-source machine learning tool to help users with no prior coding knowledge perform operations through its helpful features. It is an easy-to-use tool. It has applications in finance, CRM, and pharmaceutical industries in data-related operations like data manipulation and mining.

It is a perfect start for beginners in AI and machine learning to perform powerful analytical operations on its GUI workflow easily.


  • It offers the ability to evaluate different programming language codes in the system.

  • It is a great alternative to SAS.

A great machine learning tool contains suitable user interfaces and extensive libraries and supports the programming languages commonly used. However, you should remember that no tool can work for every algorithm or model in machine learning. Therefore, you need at least basic knowledge of the tool's functions and its purpose.

Strong fundamental knowledge of machine learning is essential. Machine learning is about creating models capable of working without human intervention; therefore, this discipline requires you to practice new and advanced tools constantly.

Well, you might be worried about where to start.

You can enroll in the advanced AI and ML program. Such a program will not only help you gain the knowledge of tricky tools usages for ML but also will offer real-world project experiences. The mentioned course is associated with dedicated support from top-rated industrial experts along with amazing project benefits.

Mastering the most used tools for machine learning under expert supervision will help you stay in the limelight of tough job market competition.