Back to blog

Top 5 best machine learning tools [Update 2022]

Sep 03, 2021

7 minutes read

As the world progressively shifts digital, Artificial Intelligence (AI) has significantly altered the way humans work for the better.

Because of the constant emergence of online technology, all aspiring tech gurus must keep up, especially if they want to maintain their name in the business. “Machine learning” is one of the newest buzzwords that is gaining traction.

Due to the ever-increasing volume of data created and consumed, machine learning models are in high demand. Machine learning systems help fortify data analysis and decision-making automation by incorporating algorithms and sophisticated machine learning libraries into the system. It is no surprising that machine learning is gaining traction, as machine learning applications now pervade practically every facet of modern life.

What is machine learning?

Machine learning is an interdisciplinary field that straddles science, mathematics, engineering, and software development. But that is not all: data scientists utilizing machine learning must understand both the problem and the domain to provide a useful solution.

Machine learning gets accompanied by a diverse set of AI and machine learning tools, platforms, and software. This article includes a list of the top 5 machine learning tools used by experts.

Related: How Machine Learning reduces costs spent on treatment

Machine learning enables computers to make better decisions faster and, in most cases, with greater accuracy. This method is low-cost and capable of analyzing huge and complex data sets.

Notably, there are three types of machine learning:

  • Supervised machine learning: In supervised machine learning, past data gets used to produce predictions. For example, supervised machine learning gets used to filter spam emails. To determine which emails are spam and which are not, machine learning algorithms get used. The supervised machine learning algorithm predicts whether an email is a piece of spam based on past data such as received emails and data that users use. These predictions are not always correct, but they are accurate.
  • Unsupervised machine learning: Unsupervised machine learning uncovers previously unknown patterns. This form of machine learning includes clustering and association techniques.
  • Reinforcement machine learning: Reinforcement machine learning is a type of machine learning used to improve or increase efficiency.

Machine learning tools comparison: pros and cons

There is a wide range of machine learning Software on the market. Below is a list of the top machine learning tools today.


Scikit-Learn is a free Python machine learning library. It contains models and techniques for classification, regression, clustering, dimensional reduction, model selection, pre-processing, and data mining and data analysis.

Scikit-Learn is a collection of machine learning and statistical modeling tools built on NumPy, SciPy, and Matplotlib.


The machine learning library Scikit-Learn has a lot of features that make machine learning easier. Here are a few of them:

  • Any supervised machine learning algorithm that people have heard of is likely to be included in the Scikit-Learn library.
  • Factoring, cluster analysis, principal component analysis, and unsupervised neural networks are among the algorithms included in this machine learning tool collection.
  • You can use Scikit-Learn to test the accuracy and validity of supervised models on unseen data.
  • It is possible to combine the predictions of numerous supervised models using the Ensemble methods feature.


The following are the pros of opting for the Scikit-Learn machine learning tools:

  • The library gets released under the BSD license, causing it to be free, but with only the most basic legal and licensing constraints.
  • It is simple to use.
  • The Scikit-Learn machine learning library is extremely adaptable and useful. It can be a source of rich data for various real-world tasks such as predicting consumer behavior, creating neuroimages, etc.
  • Numerous authors, contributors, and a large international online community support and update the Scikit-learn machine learning tool.


The Scikit-Learn machine learning tools have one pressing disadvantage. It is not the best source or model for in-depth learning. So, more complex tasks can be difficult, if not impossible, to execute.



PyTorch is an open-source machine learning tool based on Facebook’s AI Research lab’s Torch library, useful for computer vision and natural language processing applications. The Autograd Module in this open-source machine learning library aids in the construction of neural networks. The open-source PyTorch software may be utilized on cloud platforms and enable distributed training within multiple tools and libraries.


The PyTorch machine learning tools based on the Torch library have many features that make using machine learning tools easier. Here are some of this model software’s features:

  • The Autograd Module aids in the creation of neural networks for easier analytics.
  • It comes with several optimization algorithms for constructing neural networks to aid analytics.
  • PyTorch’s development allows its use on cloud platforms.
  • It offers online instruction, as well as a variety of tools and resources.


The following are the pros of opting for the PyTorch machine learning tools:

  • It is based on Python, which is the most basic programming language.
  • Deep Learning training that is more efficient.
  • GPU-based dynamic method.
  • The productivity of developers has increased.
  • It is a lot easier to learn.


Here are some of the PyTorch software model’s disadvantages:

  • In terms of production, it lacks model serving.
  • The PyTorch software model is relatively brand new and not commonly known.
  • It lacks monitoring and visualization interfaces, which may have an impact on analytics.



TensorFlow is one of the free machine learning tools running on Linux, Mac OS, and Windows platforms. It includes a JavaScript library for machine learning.

It aids users in the creation and training of their models.

TensorFlow is a fantastic machine learning tool for deep learning and neural networks. Users can also use TensorFlow.js, a model converter, to execute their current models.


TensorFlow, one of the best tools used for machine learning, comes with a slew of features that make using online machine learning tools a breeze. Here are a few of this software’s features:

  • Aids in the training and development of models.
  • TensorFlow.js is a model converter that allows users to execute their current models.
  • It is beneficial to the neural network.


The following are the pros of opting for the TensorFlow machine learning tools:

  • It may be used for deployment in two ways: via script tags or NPM installation.
  • It can even aid in the evaluation of human pose estimation.


The TensorFlow machine learning tools have one pressing disadvantage. It is an extremely difficult software model to learn. So, expect a steep learning curve before TensorFlow becomes useful.


Apache Mahout

Apache Mahout is a distributed linear algebra framework and an open-source machine learning tool that lets mathematicians, statisticians, and data scientists execute their algorithms for more efficient testing methods and analytics. The Apache Software Foundation project aims to provide open implementations of distributed or otherwise scalable machine learning algorithms, concentrating on linear algebra.


As one of the open-source machine learning tools focused on linear algebra, Apache Mahout comes with a slew of capabilities that allow the simpler use of machine learning tools. Here are a few of them:

  • Pre-processors, Regression, Clustering, Recommenders, and Distributed Linear Algebra are all covered.
  • Java libraries get included for common math operations.
  • The MapReduce paradigm gets implemented on top of Apache Hadoop.


The following are the pros of opting for the Apache Mahout machine learning tools:

  • For better analytics, it works with massive data sets.
  • The software model is straightforward.
  • These tools are extensible.


Here are some of the disadvantages of the Apache Mahout machine learning tools:

  • More useful documentation is required.
  • Certain algorithms are missing.



Weka is an open-source machine learning software for data mining. A graphical user interface (GUI), normal terminal apps, or a Java API can all be used to access it. Weka is a set of data visualization tools and algorithms for data analysis and predictive modeling widely used in education, research, and industry.


Weka machine learning tools have a lot of features that make the use of machine learning tools better. Here are a few of them:

  • Association rules mining;
  • Classification;
  • Clustering;
  • Data preparation;
  • Regression; and
  • Visualization.


The following are the pros of opting for the Weka machine learning tools:

  • Provides online training classes.
  • Straightforward algorithms.
  • It is also beneficial to students.


The Weka machine learning tools have one main disadvantage. It is only capable of handling small datasets.



Ultimately, the best AI and machine learning tools get determined by the algorithm’s specific requirements, the user’s level of knowledge, and the cost of the machine learning tools.

For more information on how your company can integrate machine learning tools to meet your specific needs, please reach out to our experts. VITech provides R&D, software, and data engineering services to cater to the needs of innovative startups.

Need software development for the healthcare industry? Leave a request!

Leave us a message

You’re in a good company:

More articles


More articles