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Machine Learning: Why is it important?

Find out how Machine Learning can be used to solve real-world problems in our essential guide.


This post is by Adil Khan, Professor of Machine Learning and a Module Leader for the Hull Online MSc AI.

What is Machine Learning? 

The main objective of Machine Learning is to build machines that can learn from data and make predictions. It is usually a two-stage process.


During the training stage, a Machine Learning model is created by applying a learning algorithm to training data for carrying out a specific task, such as cancer prediction. This model aims to generalise to new data; predicting cancer occurrence in a new patient.


Therefore, during the validation stage the Machine Learning model's effectiveness is evaluated on independent test data using metrics such as accuracy, precision and recall.


Depending on the underlying learning method, a Machine Learning model can be classified as either a deep model (such as Artificial Neural Networks) or a shallow model (such as support vector machines). 


MSc in AI Programme Director Rameez Kureshi discusses why AI has become so prevalent:


Why is Machine Learning important? 

  • Imagine a farmer who manages and cultivates crops and/or raises livestock for food, fuel, or other purposes, making use of natural resources, labor, and machinery.
  • Picture a lawyer who provides legal advice and representation to individuals, businesses, and government entities, and uses legal knowledge and skills to solve problems and achieve client goals.
  • Consider a baker prepares and bakes a variety of baked goods, including breads, cakes, pastries, and more, using specialised equipment and ingredients.


These are examples of three different professions with different objectives and skill sets. Yet, they have one thing in common: Machine Learning.


Let me explain:


  • A farmer can use Machine Learning for tasks such as crop yield prediction, soil analysis, weather forecasting, disease detection in crops, and optimizing irrigation systems to improve farming efficiency and productivity.
  • A lawyer can use Machine Learning for tasks such as document analysis, legal research, predictive analysis of case outcomes, and automating contract review and classification.
  • A baker can use Machine Learning for tasks such as recipe optimisation, predicting ingredient requirements, forecasting demand, and optimizing production schedules. 


Such wide applicability of Machine Learning in a wide range of professions is because it provides a set of algorithms and statistical models that can learn from data, identify patterns, and make predictions or decisions based on that data.


Therefore, any profession that produces data, which is every profession in today’s world, can employ Machine Learning for increasing revenue, task optimisation, automated decision making, etc.   


It is for these reasons that Machine Learning has become the top most emerging trend in computing and information technology. Each year, over 30,000 research papers are published across hundreds of journals and conferences.


Besides creating a large number of new Machine Learning-focused startups, almost all traditional industries, from agriculture to transportation, are already being impacted by Machine Learning. With the tools available today, every employee can leverage machine intelligence to increase productivity.


For the first time, businesses have access to the complete set of building blocks needed to start integrating machine intelligence into their operations. This change of landscape is resulting in high demand for Machine Learning engineers in all sorts of industries. 


Machine Learning has become an essential skill to learn for almost everyone. However, in order to be able to effectively use Machine Learning for solving real-world problems, one must develop a thorough understanding of what machine learning is, what kind of models and algorithms does it provide to us for solving problems, what makes one model better than the other and under what circumstances, etc.


That is why it is important to not just learn how to simply apply these models, using Machine Learning libraries such as scikit-learn, tensorflow, pytorch, etc., but also to learn the theoretical underpinning of these models. Keeping this in mind, we have designed the Machine Learning module so that it gives students both perspectives. 


Explore how global organisations use Machine Learning during the Hull Online MSc in AI: 


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