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Top 10 Machine Learning Tools in the Software Industry

Top 10 Machine Learning Tools in the Software Industry

Machine learning (ML) is one of the most up-and-coming areas in tech. Using machine learning, computers can learn to recognize patterns in data, images, audio clips, etc. As more ML-reliant applications increase each day, new methodologies are being developed to implement the same. In this article, we explore the top 10 machine learning tools that both professionals and novice ML practitioners are using the world over.

The 10 best machine learning tools

While there are many tools used in machine learning, the following 10 are among the most preferred.


logo of scikit learn.webp

First developed by David Cournapeau in Google Summer of Code (GSoC) 2007, sklearn (also called scikit-learn) is one of Python's most popular and robust tools for performing machine learning-related tasks. With the help of its efficient tools, one can perform operations like classification, regression, clustering, data preprocessing, model selection, dimensionality reduction, statistical modeling, data mining, and data analysis.

Mostly written in Python and built upon libraries like NumPy, sciPy, and Matplotlib, sklearn also has several beginner-friendly standard datasets to help those just starting out to practice its codes and commands. These datasets include the Boston House Prices dataset, Iris Dataset, Diabetes Dataset, Linnerud dataset, and wine recognition dataset, among others.


Using pip

pip install -U scikit-learn

Using conda

conda install scikit-learn

Note: Your system should already have NumPy, sciPy, Matplotlib, and Pandas installed for sklearn to work.


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PyTorch is an open-source machine learning framework developed by Meta’s AI research lab. Mainly used for deep learning, the two most popular features of Pytorch are accelerated processing for tensor computing and tape-based autograd system for neural networks. The autograd module helps in building optimized neural net paths for faster tensor computation since all the input data in PyTorch is in the form of tensors.

PyTorch has interfaces for both Python and C++. Being a Torch-based library, it also provides many machine learning tools and libraries like distributed training, AllenNLP, ELF, etc.

There are many deep learning frameworks available in the market but the most popular are TensorFlow and PyTorch, with the latter inching towards being the best since it's easier to learn, is faster, and flexible.

PyTorch is used in several technologies like computer vision (to build convolutional neural networks for image classification and object detection), natural language processing or NLP (to develop RNN, LSTM models for chatbots, language translation, and sentiment analysis), and reinforcement learning.


The installation command depends on one’s system and preferences. It can easily be found on the official PyTorch website.


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TensorFlow is an open-source machine learning framework first developed by the engineers and researchers of Google Brain. The library was initially made for research purposes in ML and deep neural networks. However, after development was complete, it was found that it could be applied in numerous other fields as well, owing to its generality and flexibility.

TensorFlow includes several pre-built models that can be used to solve minor problems. Data flow graphs, which are particularly useful when developing complex models, are one of its most appealing features. Some other important features of TensorFlow are easy model development, complex numeric calculations, useful APIs, using GPU for processing, Keras support, inbuilt visualization tools, etc.

The framework has applications in various fields like facial recognition systems, speech recognition, self-driving vehicles, natural language processing, sentiment analysis, and recommendation systems.


Both Anaconda and pip can be used to install TensorFlow. Here are the commands:

> conda create --name tensorflow python = 3.5

After installation, activate the module:

> activate tensorflow

To install using pip:

> pip install tensorflow

> pip install tensorflow-gpu


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Colaboratory by Google, sometimes referred to as Google Colab, is a free cloud platform for machine learning and data science. It solves the physical limitations one might face when working with machine learning models. For example, not everyone has access to a powerful device with a dedicated GPU to run sophisticated models and algorithms. This is where Colab comes in handy since free options for GPUs and TPUs are available while important libraries are preinstalled to ensure a hassle-free work environment.

Some of the features of Google Colab are:

  • Supports all the major versions of Python.
  • Free GPU and TPU acceleration is available.
  • Major libraries like Pandas, NumPy, Matplotlib, TensorFlow, sklearn, etc. are preinstalled.
  • Interface, shortcuts, and utilities are the same as Jupyter Notebook since it is built on top of it.
  • Supports collaboration among users.
  • Bash commands are available.
  • All the notebooks are available on Google Drive.

Amazon Web Services

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Amazon Web Services or AWS is a cloud-based platform with distributed IT infrastructure to provide services like IaaS (infrastructure as a service), SaaS (software as a service), and PaaS (platform as a service). It has a pay-as-you-go fee structure for its tools and services and was one of the first organizations to adopt and popularize this method for cloud computing.

AWS provides a wide range of tools and solutions for businesses and software engineers that may be used in server farms in over 190 countries. The services are available to government instruments, educational organizations, NGOs, and businesses. Its services can be customized according to the needs of end-users.

The Amazon Web Services portfolio includes over 100 services, including data managing, networking, big data management, AI, and application development.

Here are some of its features:

  • Flexible and cost-effective: Traditional IT infrastructure requires big capital and investment upfront. With AWS, users get to choose and customize their system requirements according to projects. The pay-as-you-go model makes AWS a very attractive option for startups and companies.
  • Scalable and secure: One can scale computing resources depending on need. Amazon also provides end-to-end security and privacy by strictly following the rules of network security triad, CIA (confidentiality, integrity, availability).

Google Cloud Platform

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Google Cloud Platform or GCP is a collection of cloud-based services offered by Google. Similar to AWS, it offers various services like AI Hub, machine learning, Cloud TPU, API management, App Engine, etc. All the services run on the same infrastructure Google uses internally for its own systems and products like YouTube, Gmail, and Chrome.

Software engineers and users with less technical expertise can quickly access and learn the utilities and capabilities of GCP. In comparison to its rivals, Google has remained on top, providing the most dependable and scalable platform for developing, testing, and deploying real-time machine learning applications.

Thanks to the availability of cost planning, reliable hardware, and sophisticated control, Google data centers are used by the majority of businesses that lack the capabilities needed to operate and manage a data center's resources themselves.

IBM Cloud

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The whole stack of IBM cloud services for business-to-business (B2B) enterprises includes more than 170 products and cloud computing tools that are available globally. The three primary service models (or varieties) of cloud computing are all included in IBM Cloud, as is the case with many other all-encompassing cloud computing services including AWS, Microsoft Azure, and Google Cloud. These include SaaS, PaaS, and more recently, IaaS. Additionally, it is made available through the hybrid, private, and public cloud deployment types.

IBM Cloud has some of the best machine learning APIs to date and includes some of the most crucial business services like NLP, deep learning APIs, mobile application development, voice recognition, image recognition, and chatbots.


logo of anaconda.webp

Anaconda is a Python and R programming language distribution that is free, open-source, and simple to install. It provides a fantastic workspace for data science and machine learning along with statistical programming with R.

Anaconda Navigator is a GUI for the desktop that comes with the Anaconda distribution. It enables one to run programs from the Anaconda distribution and manage various Python modules and packages, environments, and channels without using command-line tools. It is compatible with Windows, macOS, and Linux.

A number of applications come pre-installed with Anaconda. These include Jupyter Notebook, JupyterLab, Spyder (Python IDE), VS Code, etc. Important ML frameworks such as TensorFlow and scikit-learn are also preinstalled.


logo of weka.webp

Weka (Waikato Environment for Knowledge Analysis) is an open-source toolkit accessible under the GNU GPL (General Public License) that contains data preprocessing tools, implementation of numerous ML algorithms, and visualization tools to help build machine learning models and apply them to real-world data mining situations.

Weka is a data mining workstation that includes machine learning algorithms. These tools can help build end-to-end ML projects, with duties ranging from data preparation and data visualization to classification and clustering. Weka’s specialty is classification, but it can also conduct regression, clustering, and association rule mining.

Here are a few features of Weka:

  • Open-source: It is distributed as free software under GNU GPL. Pentaho Corporation owns the sole permission to use the platform for business analytics in its own services.
  • GUI: It includes a user-friendly GUI which enables one to finish machine learning tasks without having to know how to program.
  • CLI commands: Using the cmd (command line), one can access all of the software's capabilities. This is helpful when writing massive processes.
  • Java APIs: It is developed in Java and has a well-documented API that encourages integration into one’s own applications.


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Shogun is a C++-based open-source framework for machine learning. It provides a large selection of comprehensive machine learning algorithms that are both optimized and effective. Shogun's kernel machines, which are used to solve regression and classification issues, include support vector machines.

A complete application of hidden Markov chains is available in Shogun. It is efficiently configured for MATLAB, Octave, Python, R, Java, Lua, Ruby, and C#. Its core is developed in C++. Today, Shogun is used as a foundation for research and education throughout the globe by a sizable and active user base that also contributes to the core package.


With conda:

> conda install -c conda-forge shogun
Installing on Ubuntu

>sudo add-apt-repository ppa:shogun-toolbox/stable
>sudo apt-get update

>sudo apt-get install libshogun18

>sudo apt-get install python-shogun

What we’ve explored in this machine learning tools list only scratches the surface of the technologies present for ML. Using them, we can model how data behaves and make predictions for the future. All these machine learning tools have applications in various fields like business intelligence, medical analysis, digital marketing, marketing analysis, etc. With so many technologies available, however, it’s easy to get overwhelmed. The best approach is to know which technologies are necessary in the field we are about to enter, start from there, and build the required skill sets.


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Frequently Asked Questions

Every machine learning tool has some advantage over the other and excels in different areas but the most popular and widely used are sklearn, TensorFlow, PyTorch, and Anaconda since they cover most of the utilities one might need for a machine learning project.

Both are very useful tools for machine learning but when it comes to popularity and user base, PyTorch wins as it is widely adopted by the community of machine learning practitioners.

Software powered by machine learning can use previous data to predict the future. This gives such software an edge over traditional ones where every bit of utility is hard coded.

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