6 machine learning tools (and what they're used for)

By Indeed Editorial Team

Published 14 November 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

As technology advances and becomes more prevalent in our lives, it's increasingly vital that organisations use technology to provide better services for users or optimise their work processes. Artificial learning and algorithm-building are key ways of providing both of these benefits, using complex computing to process and analyse large amounts of data quickly. The concept of machine learning is a form of this artificial learning and requires the use of specific tools to operate effectively. In this article, we explore what machine learning tools are, list seven examples of tools and discuss some of their uses.

What are machine learning tools?

Machine learning tools use artificial intelligence (AI) and algorithms to collect, evaluate and use large amounts of data. From these findings, an organisation is able to optimise the way it works, deliver a better service for end users or improve specific products or services. By using machine learning, an organisation is able to analyse this data and quickly identify potential actions, while the software builds experience and executes actions without the need for manual input.

Related: What does a machine learning engineer do? (With skills)

6 examples of machine learning software

Machine learning software allows an organisation to start using machine learning activities. While these tools require a set-up period from someone experienced in programming and computing, they eventually build their own experience in certain tasks and evolve without needing any additional programming support. By choosing the right tools, an organisation is able to automate a range of tasks and make the entire process more efficient. Below are six examples of machine learning software:

1. IBM Watson

IBM Watson is a specific tool that uses IBM cloud services. Watson Machine Learning uses different data points to produce machine learning and deep learning models, which an organisation is able to use. A common tool for machine learning, IBM Watson allows the user to perform two critical functions, which are training and scoring.

IBM Watson is useful across a variety of industries as it is capable of analysing huge amounts of data incredibly quickly. While it's commonly used in specialist areas such as cybersecurity, it also has applications in marketing that require deep analytics. IBM Watson is also used to analyse human speech and responses to build meaning and an understanding of how to answer human-posed queries. It provides answers to questions that an organisation may not understand and uses machine learning to improve its knowledge of certain subjects to deliver better insights.

Related: 5 common jobs in machine learning (and how to find them)

2. KNIME

KNIME is a tool that assists with data analysis and analytics reporting alongside integration across multiple platforms. As a tool, it uses the concept of data pipelining and combines various components for both machine learning and data mining. KNIME is unique as it integrates with programming languages such as C, C++ and Python while also being useful for providing financial data analysis, CRM and further business intelligence.

Related: Deep learning vs. machine learning: definitions and types

3. Microsoft Azure Machine Learning

Azure Machine Learning is a cloud-based platform that helps developers build and train AI models ready for machine learning. Azure Machine Learning is particularly useful for data scientists, engineers or other machine learning professionals that use machine learning in their daily operations. A crucial part of Azure Machine Learning is its ability to deploy AI models, providing an easy way of ensuring the tool is integrated correctly and operating efficiently. Once an organisation deploys the model, Azure Machine Learning monitors its lifecycle and provides audited reporting ready for reproduction or improvement.

Related: What are the 4 different types of machine learning?

4. Open Neural Networks Library

Open Neural Networks Library (OpenNN) is a full software library comprised of neural networks. As an open-source tool, OpenNN is free to download and written entirely in the programming language C++. This is a general-purpose tool that helps individuals solve predictive analysis tasks across fields such as chemistry, energy and engineering. With a library that includes a range of functions, users may embed OpenNN into other tools, such as APIs, and use it to assist with predictive analytics.

Related: 12 careers in AI (plus responsibilities and salaries)

5. Scikit-learn

Scikit-learn is a tool developed in Python and includes a full library for that specific programming language. Scikit-learn is commonly used for large-scale data analysis and data mining, with models for classification, regression, model selection, clustering and dimensional reduction. As a free tool, it's easy to understand and flexible, as any parameters for models or algorithms may change at a moment's notice.

6. Weka

Weka is an open-source data mining software tool useful for machine learning. Weka supports machine learning algorithms alongside data preparation, with a user-friendly interface that also provides various machine learning techniques. The main pros of Weka are the integrated data preparation, feature selection and data mining algorithms. Weka is a lightweight tool that also includes ready-made code alongside a library of processed techniques and example models.

Related: How to become a machine learning engineer in 4 steps

What are machine learning tools used for?

There are certain fundamental approaches to machine learning that these tools either support or provide. The model and tool that a data scientist or engineer uses are dependent on the data they want to predict or learn more about. Below are the basic approaches to machine learning:

Supervised learning

This is a common form of machine learning wherein a data scientist provides an algorithm with set training data and parameters that they want the algorithm to assess. Through this, the operator is able to define the input and output of the algorithm, which helps the algorithm build experience around subjects, predicting or forecasting. Supervised learning is useful for helping an algorithm with the following tasks:

  • Binary classification: The act of dividing data into two separate classifications or categories.

  • Multi-class classification: The act of dividing data into more than two types of classifications and providing answers.

  • Regression modelling: Predicting continuous values for use in forecasting and other large-scale prediction models.

  • Ensembling: Combining multiple predictions from various machine learning models to create an overall prediction that's accurate and reliable.

Related: How to become a cloud engineer (with skills and salaries)

Unsupervised learning

Unsupervised learning is a useful form of machine learning for individuals who don't have set analysis patterns. It allows the algorithm to analyse any unlabelled data to identify trends or patterns that it then groups into specific datasets. Unsupervised learning is commonly used in deep learning and often requires the tool to build experience over time as it picks out specific trends or patterns. The engineer typically predetermines the output of the algorithm. Unsupervised learning algorithms help with:

  • Clustering: Segmenting any data into specific groups based on similarities recommended by the algorithm.

  • Anomaly spotting: Identifying outliers or anomalies within the data set may provide information around a specific data point.

  • Association: Identifying any patterns or trends that frequently occur within the large dataset.

  • Dimensionality reduction: Reducing the number of variable data points within a larger dataset.

Related: Analytical skills: definitions and examples

Semi-supervised learning

Semi-supervised learning is machine learning that uses both supervised and unsupervised learning concepts. In this approach, an engineer may provide the algorithm with training data but allow it to explore the data and develop recommendations of its own accord. This way of learning allows the tool to understand the parameters and dimensions of the data that it may then apply to any unlabelled, raw data. This is an option for engineers who want both the performance of a supervised learning model and the efficiency of unsupervised learning. It helps with:

  • Labelling data: By training an algorithm on small datasets of labelled data, it might learn to apply these same labels to much larger datasets, which alleviates a time-intensive task.

  • Fraud detection: Semi-supervised learning may identify fraud cases when an individual only has a few positive examples.

  • Machine translation: A common reason for semi-supervised learning is teaching an algorithm to translate a full language based on a reduced number of words.

Please note that none of the companies, institutions or organisations mentioned in this article are affiliated with Indeed.

Related:

  • How to use IBM doors (overview and step-by-step guide)


Explore more articles