Machine Learning Definition, Basics, Tools, Types, Comparison

Machine learning is the simulation of human learning processes by software algorithms, through the collection and analysis of data. This article discusses machine learning definition, basics, tools, types, and comparison, as follows;



-Machine Learning Definition: 3 Ways to Define Machine Learning

-Basics of Machine Learning

-Tools for Machine Learning

-Types of Machine Learning

-Machine Learning Vs Deep Learning







Machine Learning Definition: 3 Ways to Define Machine Learning

Machine learning (ML) is a branch of artificial intelligence that specializes in the simulation of human learning processes in software systems through active data analysis and classification [4].

The above is a basic outline of the concept of machine learning. A more specific machine learning definition is given below, which describes it from the algorithmic perspective;

Machine learning algorithm refers to a distinct type of artificial intelligence software, whose function involves analyzing multiple layers of data on a spatial and/or temporal basis to establish fortified neural networks that link datasets to one another through pattern identification [2].

Machine Learning Definition: Analaysis of Mulitple Data Layers (Credit: TseKiChun 2021 .CC BY-SA 4.0.)
Machine Learning Definition: Analaysis of Mulitple Data Layers (Credit: TseKiChun 2021 .CC BY-SA 4.0.)



Some applications of machine learning are listed in the alternative machine learning definition below;

Machine learning is an algorithm-based functionality whereby datasets are analyzed independently and comparatively to identify attributes which can be linked to form a learn-able and applicable pattern, as used in automated geospatial analysis, image and speech recognition, visually-controlled robotic navigation, and other forms of predictive analysis [3].





Basics of Machine Learning

The 4 basics of machine learning are;

1). Data collection

2). Data compilation

3). Analysis and classification

4). Data utilization


These are the most essential aspects of the working principle of machine learning algorithms.





Tools for Machine Learning

Machine learning tools are software algorithms which can enable digital systems to perform data analysis and utilization in a manner that is similar to human learning processes, through recurrent-pattern recognition, predictive implementation, compilation and continuous improvement.


Some tools for machine learning are;

1). PyTorch

2). TensorFlow

3). Colab

4). Google Cloud AutoML

5). Scikit-learn


7). XGBoost

8). IBM Watson Studio

9). Kira


These tools all occur as software applications that can be used in digital systems for machine learning functions.





Types of Machine Learning

The three main types of machine learning are;

1). Supervised Machine Learning

2). Unsupervised Machine Learning

3). Reinforcement machine learning

In some studies, a fourth type; semi-supervised machine learning, is included [1].





Machine Learning Vs Deep Learning

Deep learning is a variant of machine learning, with advanced capabilities for analyzing several data layers simultaneously.

The difference between machine learning and deep learning is in their functionality and complexity; where machine learning is equipped to perform less-complex data computations than deep learning.






Machine learning is an artificial intelligence functionality that involves the compilation and analysis of datasets to identify patterns progressively, in a manner similar to the learning process of the human brain.

The 4 basics of machine learning are; data collection, compilation, analysis, and utilization.

Tools for machine learning are; PyTorch, TensorFlow, Colab, Google Cloud AutoML, Scikit-learn, KNIME, XGBoost; IBM Watson Studio, and Kira.

Types of machine learning are; supervised, unsupervised and reinforcement machine learning.






1). Reddy, Y. C. A.; Pulabaigari, V.; Reddy, E. B. (2018). “Semi-supervised learning: a brief review.” International Journal of Engineering & Technology 7(1-8):81. Available at: (Accessed 1 January 2023).

2). Sarker, I. H. (2021). “Machine Learning: Algorithms, Real-World Applications and Research Directions.” SN Comput Sci. 2021;2(3):160. Available at: (Accessed 1 January 2023).

3). Tehrani, F. S.; Calvello, M.; Liu, Z.; Zhang, L. M.; Lacasse, S. (2022). “Machine learning and landslide studies: recent advances and applications.” Natural Hazards 114(1). Available at: (Accessed 1 January 2023).

4). Tiwari, T.; Tiwari, T.; Tiwari, S. (2018). “How Artificial Intelligence, Machine Learning and Deep Learning are Radically Different?” Available at: (Accessed 1 January 2023).

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