top of page

IbisViz8 – 8 Machine Learning Tools

With algorithms and machine learning tools, you can quantify the analysis and build a predictive model for your business. There are several tools that help in employing ML techniques like linear regression, decision trees, classifier algorithms and others to understand why something went wrong and then predict the future based on both the past and present data. There are a lot more ML tools than the list below and we have just chosen to highlight eight of them to demonstrate some of the capabilities that exist. We do not personally or professionally endorse any of the products.

1. Jupyter Notebook

Jupyter Notebook is one of the most widely used machine learning tools. It is a very fast processing as well as an efficient platform. Jupyter Notebook allows the user to store and share the live code in the form of notebooks. It can also be accessed through a GUI.

2. Pytorch

Pytorch is a deep learning framework. It is very fast as well as flexible to use. This is because Pytorch has a good command over the GPU (graphics processing unit). It is one of the most important tools of machine learning because it is used in the most vital aspects of ML which includes building deep neural networks and tensor calculations.

3. RapidMiner

One of the most popular tools for non-programmers. It supports all the data science steps starting from data preparation, machine learning, deep learning, text analytics, model validation, data visualization, and predictive analytics. It has a very good graphical user interface. RapidMiner is platform-independent as it works on cross-platform operating systems.

4. Knime

Another one that is very good for non-programmers. Knime is an open-source machine learning tool that is based on GUI. KNIME is popular because it uses natural language for processing, and that is why it is easier to understand. Because it is built on modular data pipelining concept, it is generally used for data relevant purposes, like data manipulation, data mining, etc.

5. Weka

Weka is a collection of machine learning algorithms that can be applied directly to a dataset or called from your code. It contains tools for data mining tasks, including data preprocessing, regression, classification, clustering, association rules. It has a nice GUI that will help beginners to understand machine learning well and can be used in combination with R programming language.

6. Accord.NET

The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications. Such packages assist in training the models and in creating interactive applications.


MATLAB is a simple yet powerful tool that can be learned easily. It is used for plotting of data and functions, manipulations of matrices, implementation of algorithms and creation of user interfaces. MATLAB combines a desktop environment tuned for iterative analysis and design processes with a programming language that expresses matrix and array mathematics directly. If you have prior knowledge of C, C++ or Java, learning and implementing ML algorithms in MATLAB would be easy.

8. TensorFlow

TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. Complex ML models can be trained, iterated and retrained quickly to make them ready for production.

This is our list of some of the most widely used ML tools. Data science is a field that is becoming more-and-more ubiquitous. The tools are making it easier for analysts to deploy complex models without the need for data scientists. Having the skills and knowledge in some of these can be valuable as data technology machine learning seeps further into all areas of organizations.

4 views0 comments
bottom of page