What is Machine Learning?

Written by Shrisha Sapkota
Written by Shrisha Sapkota


case management software, practice management software, legal accounting software, legaltech, technology for lawyers, case management, immigration, london, united kingdomcase management software, practice management software, legal accounting software, legaltech, technology for lawyers, case management, immigration, london, united kingdom

Machine learning is an exciting branch of Artificial Intelligence (AI), and it’s all around us.[1] The robot-depicted world of our not-so-distant future relies heavily on our ability to deploy AI successfully but transforming machines into thinking devices is not as easy as it may seem and strong AI can only be achieved with machine learning to help machines understand as humans do.[2]

Machine learning is a branch of artificial intelligence and computer science that focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.[3] Machine learning algorithms build a model based on sample data, known as training data, to make predictions or decisions without being explicitly programmed to do so and are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.[4]

Machine learning brings out the power of data in new ways, such as Facebook suggesting articles in your feed.[5] Machine learning algorithms are used in a wide variety of applications, such as in medicine, email spam filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.[6] Some other popular uses of it include recommendation engines, fraud detection, malware threat detection, business process automation, and predictive maintenance.[7]

Machine learning is an important component of the growing field of data science.[8] Because data science is a broad term for multiple disciplines, machine learning fits within data science.[9] Through the use of statistical methods – such as regression and supervised clustering, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects.[10] On the other hand, the data in data science may or may not evolve from a machine or a mechanical process.[11] These techniques produce results that perform well without programming explicit rules.[12]

The concept of machine learning has been around for a long time (like the World War II Enigma Machine) but the idea of automating the application of complex mathematical calculations to big data has only been around for several years, though it’s now gaining more momentum.[13]

Why Is Machine Learning Important

Machine learning as a concept has been around for quite some time.[14] Resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever – things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage.[15]

The term “machine learning” was coined by Arthur Samuel, a computer scientist at IBM and a pioneer in AI and computer gaming.[16] Robert Nealey, the self-proclaimed checkers master, played the game on an IBM 7094 computer in 1962, and he lost to the computer – compared to what can be done today, this feat almost seems trivial, but it’s considered a major milestone within the field of artificial intelligence.[17]

Machine learning has become a significant competitive differentiator for big organisations and many of today’s leading companies, such as Facebook, Google, and Uber, make machine learning a central part of their operations.[18] As a discipline, it explores the analysis and construction of algorithms that can learn from and make predictions on data.[19]

All of these things mean it’s possible to quickly and automatically produce models that can analyse bigger, more complex data and deliver faster, more accurate results – even on a very large scale – and by building precise models, an organisation has a better chance of identifying profitable opportunities – or avoiding unknown risks.[20]

To put it simply, machine learning is important because it gives enterprises a view of trends in customer behaviour and business operational patterns, as well as supports the development of new products.[21] It has also changed the way data extraction and interpretation are done by automating generic methods/algorithms, thereby replacing traditional statistical techniques.[22]

In this way, machine learning can be the key to unlocking the value of corporate and customer data and enacting decisions that keep a company ahead of the competition.[23] It has several practical applications that drive the kind of real business results – such as time and money savings – that have the potential to dramatically impact the future of your organisation.[24]

What are the approaches to Machine Learning?

In a world saturated by artificial intelligence, machine learning, and over-zealous talk about both, it is interesting to learn to understand and identify the types of machine learning we may encounter.[25] On the research side of things, machine learning can be viewed through the lens of theoretical and mathematical modelling of how this process works but more practically it is the study of how to build applications that exhibit this iterative improvement.[26] As with any method, there are different ways to train machine learning algorithms, each with its own advantages and disadvantages.[27] There are many ways to frame this idea, but[28] Traditionally machine learning approaches are divided into three broad categories, depending on the nature of the “signal”, “label” or “feedback” available to the learning system.[29] The three types are supervised learning, unsupervised learning, and reinforcement learning. Each one has a specific purpose, and action, yielding results and utilising various forms of data.[30]

Supervised Learning

Supervised learning is the most popular paradigm for machine learning.[31] As its name suggests, Supervised machine learning is based on supervision.[32] Known or labelled data are used for the training data and since the data is known, the learning is, therefore, supervised, i.e., directed into successful execution.[33] Here, the labelled data specifies that some of the inputs are already mapped to the output.[34] Both the input and the output of the algorithm are specified.[35]

Even though the data needs to be labelled accurately for this method to work, supervised learning is extremely powerful when used in the right circumstances.[36] Supervised learning helps organisations solve a variety of real-world problems at scale, such as classifying spam in a separate folder from your inbox.[37]

Unsupervised Learning

Unsupervised learning is very much the opposite of supervised learning – it features no labels.[38] It describes a class of problems that involves using a model to describe or extract relationships in data.[39] This means that human labour is not required to make the dataset machine-readable, allowing much larger datasets to be worked on by the program.[40] It aims to make groups of unsorted information based on some patterns and differences even without any labelled training data.[41]

Unsupervised machine learning algorithms are used when the information used to train is neither classified nor labelled.[42] Its ability to discover similarities and differences in information makes it the ideal solution for exploratory data analysis, cross-selling strategies, customer segmentation, image and pattern recognition.[43] A central application of unsupervised learning is in the field of density estimation in statistics, such as finding the probability density function.[44]

Reinforcement Learning

Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment to maximise some notion of cumulative reward.[45] It works on a feedback-based process, in which an AI agent (A software component) automatically explores its surroundings by hit and trial, taking action, learning from experiences, and improving its performance.[46]

Reinforcement learning is very behaviour-driven and has influences from the fields of neuroscience and psychology – specifically, the concept of Pavlov’s dog.[47] It is often used in areas like robotics – where bots can learn to perform tasks in the physical world, video gameplay – where it is used to teach bots to play several video games, and resource management – where enterprises plan out how to allocate the finite resources they have been given to reach the defined goal.[48]

Machine Learning in Law

The legal profession, the technology industry, and the relationship between the two are in a state of transition.[49] At first, you might find the idea of machine learning or artificial intelligence and law working together as very unlikely since both the fields appear to be poles apart – however, the truth is far from it.[50] Today, the legal tech landscape is rich with machine learning and AI solutions.[51]

When we think about machine learning and AI in legal tech innovation today, it is important to remember that AI is not merely one thing but a variety of technologies and task-specific applications that can assist legal professionals in the exercise of their function.[52]

AI and machine learning is transforming the legal profession in various ways, helping law firms manage their operations as well as augmenting and reducing many of the tasks that were previously relied upon by humans to do, saving precious time and manpower that can be otherwise used for more productive tasks.[53] Whilst technological gains might once have had lawyers sighing at the ever-increasing stack of documents in the review pile, technology is now helping where it once hindered.[54] Research has shown that a lot of manual work required in law firms have been substituted by artificially intelligent machines[55] used as legal technology solutions. For the first time ever, advanced algorithms – like secure document management – allow lawyers to review entire document sets at a glance, releasing them from wading through documents and other repetitive tasks which means legal professionals can conduct their legal review with more insight and speed than ever before, allowing them to return to the higher-value, more enjoyable aspect of their job: providing counsel to their clients.[56]

Computer processing power has doubled every year for decades, leading to an explosion in corporate data and increasing pressure on lawyers entrusted with reviewing all of this information.[57]

Software firms are also developing machine learning algorithms for more sophisticated legal processes related to trial preparation – some law firms use earlier court documents to make predictions about a case, such as its time to trial, the likelihood of success, and the potential damages it could win.[58]

One additional area where machine learning and AI has potential to improve access to justice is in expertise automation as some AI tools automate simple workflows in civil matters, providing wider access to the law to people who cannot afford legal counsel.[59] This feature can be found in the best legal document automation software.

AI-powered legal software and AI tools for lawyers improves the efficiency of document analysis for legal use and machines can review documents and flag them as relevant to a particular case.[60] Once the relevant documents are shortlisted and flagged, machine learning comes into work and uses the learned algorithm to find similar documents that can be of use, out of the millions of papers, proceedings, and dissents.[61]

Another big portion of work law firms do on behalf of clients is to review contracts to identify risks and issues with how contracts are written that could have negative impacts on their clients.[62] Sometimes, contracts can be misleading and have a negative impact, and legal professionals assist their clients to avoid just that but now AI can and is being used, to analyse such contracts in bulk and made to learn to identify such situations quickly with fewer human errors, avoiding any mishap.[63]

Similarly, machine learning and legal workflow automation software can aid in predicting the outcomes of litigation – through litigation management softwares – too. One of the best things about machine learning is its high-value predictions that can guide better decisions and smart actions in real-time without human intervention.[64] A handful of AI teams are building machine learning models to predict the outcomes of pending cases, using as inputs the corpus of relevant precedent and a case’s particular fact pattern.[65] Since computers and artificially intelligent systems have access to huge amounts of trial data and years of documentation, pattern recognition, and machine learning comes into play in the analysis of all that.[66]

Machine learning has proven to be one of the most game-changing technological advancements of the past decade.[67] The impact of AI on the legal profession is felt very clearly.  Its use in the legal profession has only just begun and while we have already seen its impact, its continued use and growth will shape legal strategy in the years to come.[68] Now, as the legal industry is undergoing significant change, the advent of machine learning technology is fundamentally reshaping the way lawyers conduct their day-to-day practice.[69]

As machine learning continues to increase in importance to law becoming more practical in enterprise settings, the machine learning platform wars will only intensify.[70]


[1][1] https://www.simplilearn.com/tutorials/machine-learning-tutorial/what-is-machine-learning

[2] https://www.expert.ai/blog/machine-learning-definition/

[3] https://www.ibm.com/cloud/learn/machine-learning

[4] https://en.wikipedia.org/wiki/Machine_learning

[5] https://www.simplilearn.com/tutorials/machine-learning-tutorial/what-is-machine-learning

[6] https://en.wikipedia.org/wiki/Machine_learning

[7] https://www.techtarget.com/searchenterpriseai/definition/machine-learning-ML

[8] https://www.ibm.com/cloud/learn/machine-learning


[10] https://www.ibm.com/cloud/learn/machine-learning


[12] https://www.mastersindatascience.org/learning/data-science-vs-machine-learning/

[13] https://www.simplilearn.com/tutorials/machine-learning-tutorial/what-is-machine-learning

[14] https://www.expert.ai/blog/machine-learning-definition/

[15] https://www.sas.com/en_us/insights/analytics/machine-learning.html

[16] https://www.expert.ai/blog/machine-learning-definition/

[17] https://www.ibm.com/cloud/learn/machine-learning

[18] https://www.techtarget.com/searchenterpriseai/definition/machine-learning-ML

[19] https://www.expert.ai/blog/machine-learning-definition/

[20] https://www.sas.com/en_us/insights/analytics/machine-learning.html

[21] https://www.techtarget.com/searchenterpriseai/definition/machine-learning-ML

[22] https://www.simplilearn.com/tutorials/machine-learning-tutorial/what-is-machine-learning

[23] https://www.netapp.com/artificial-intelligence/what-is-machine-learning/

[24] https://www.javatpoint.com/importance-of-machine-learning

[25] https://towardsdatascience.com/what-are-the-types-of-machine-learning-e2b9e5d1756f

[26] https://towardsdatascience.com/what-are-the-types-of-machine-learning-e2b9e5d1756f


[28] https://towardsdatascience.com/what-are-the-types-of-machine-learning-e2b9e5d1756f

[29] https://en.wikipedia.org/wiki/Machine_learning#Approaches

[30] https://www.simplilearn.com/tutorials/machine-learning-tutorial/what-is-machine-learning

[31] https://towardsdatascience.com/what-are-the-types-of-machine-learning-e2b9e5d1756f

[32] https://www.javatpoint.com/types-of-machine-learning

[33] https://www.simplilearn.com/tutorials/machine-learning-tutorial/what-is-machine-learning

[34] https://www.javatpoint.com/types-of-machine-learning

[35] https://www.techtarget.com/searchenterpriseai/definition/machine-learning-ML


[37] https://www.ibm.com/cloud/learn/machine-learning

[38] https://towardsdatascience.com/what-are-the-types-of-machine-learning-e2b9e5d1756f

[39] https://machinelearningmastery.com/types-of-learning-in-machine-learning/


[41] https://www.javatpoint.com/importance-of-machine-learning

[42] https://www.expert.ai/blog/machine-learning-definition/

[43] https://www.ibm.com/cloud/learn/machine-learning

[44] https://en.wikipedia.org/wiki/Machine_learning#Approaches

[45] https://en.wikipedia.org/wiki/Machine_learning#Approaches

[46] https://www.javatpoint.com/types-of-machine-learning

[47] https://towardsdatascience.com/what-are-the-types-of-machine-learning-e2b9e5d1756f

[48] https://www.techtarget.com/searchenterpriseai/definition/machine-learning-ML

[49] https://www.itproportal.com/features/the-impact-of-machine-learning-on-the-legal-industry/

[50] https://www.analyticsinsight.net/how-ai-and-machine-learning-are-transforming-law-firms/



[53] https://www.analyticsinsight.net/how-ai-and-machine-learning-are-transforming-law-firms/

[54] https://www.itproportal.com/features/the-impact-of-machine-learning-on-the-legal-industry/

[55] https://www.analyticsinsight.net/how-ai-and-machine-learning-are-transforming-law-firms/

[56] https://www.itproportal.com/features/the-impact-of-machine-learning-on-the-legal-industry/

[57] https://www.itproportal.com/features/the-impact-of-machine-learning-on-the-legal-industry/

[58] https://www.boyden.com/media/machine-learning-for-lawyers-5960938/index.html



[61] https://www.analyticsinsight.net/how-ai-and-machine-learning-are-transforming-law-firms/


[63] https://www.analyticsinsight.net/how-ai-and-machine-learning-are-transforming-law-firms/

[64] https://www.javatpoint.com/importance-of-machine-learning


[66] https://www.analyticsinsight.net/how-ai-and-machine-learning-are-transforming-law-firms/



[69] https://www.itproportal.com/features/the-impact-of-machine-learning-on-the-legal-industry/

[70] https://www.techtarget.com/searchenterpriseai/definition/machine-learning-ML

case management software, practice management software, legal accounting software, legaltech, technology for lawyers, case management, immigration, london, united kingdomcase management software, practice management software, legal accounting software, legaltech, technology for lawyers, case management, immigration, london, united kingdom

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