The Purpose of Machine Learning in Data Science
Machine learning, alongside other terms like artificial intelligence and big data, has become a buzzword within the Data Science industry. Considering this, it’s important to understand that machine learning and AI aren’t synonymous. Machine learning techniques are one of many applications of artificial intelligence, while both interconnected fields fall under the umbrella of Data Science.
Machine learning refers to algorithms that allow computers to learn and improve independently while they analyse data. There are several different types of machine learning, but they’re all alike in requiring no additional input from humans once they’re correctly configured.
This technology has expanded the possibilities of data science by providing a highly efficient method of analysing huge volumes of data. Tailored algorithms can help clean up messy data, identify patterns and analyse trends. Given that the total global volume of data is projected to grow to more than 180 zettabytes by 2025y, possible machine learning applications are endless.
Understanding the main components of machine learning
In machine learning, data science and AI, it’s important to understand the key components of the data analysis process. Machine learning has three main components which must be followed to gain accurate insights from data input.
Representation: Representation refers to the scope of possible models and considers factors such as the programming language used.
Evaluation: Evaluation refers to how and why certain models are preferred over others.
Optimisation: Optimisation involves applying algorithms to find the ideal represented models.
Types of machine learning
In addition to the components, there are also several subtypes of machine learning. According to IBM, each of the three types below will be most suited to different applications and scenarios. Building an understanding of each type enables data scientists to work with a wide variety of data types.
- Supervised learning
In supervised learning, data scientists provide the computer with a data set containing labelled features and an understanding of existing categories in the data. This style of learning is useful for finding patterns.
- Unsupervised learning
Unsupervised learning is useful for analysing large sets of unlabelled data. For example, email spam filters use unsupervised machine learning. In lieu of labels, unsupervised learning uses a repetitive process to categorise data according to patterns or clusters without any human intervention.
- Semi-supervised learning
Semi-supervised learning is a type of machine learning that presents a useful solution for situations where labelling an entire data set would be too costly or time consuming. A small amount of labelled data is provided, while most remains unlabelled, necessitating a combination of methods from the above learning styles.
- Reinforcement learning
Reinforcement learning is different from other styles because the algorithm doesn’t receive a sample data set for training purposes. Instead, this learning style utilises a trial and error process where feedback is received after data analysis. This method can be useful to gain an alternative perspective on a data set.
Develop your machine learning knowledge in data science
The Data Science industry offers a wealth of opportunities for career advancement, especially for those with machine learning knowledge. Business intelligence is one of the biggest industries to utilise data scientists with these skills. Insights on previously unknown patterns or trends can be extremely valuable in enabling big business to make data driven decisions and future plans. Expand the skills to succeed in the Data Science field with a Master of Data Science. This fully online course is developed to equip students with the technical skills and comprehensive knowledge needed to take a leading role in the future of Data Science. With valuable subjects like ‘Using machine learning tools’ and ‘Mathematical foundations of data science’, students will learn how to take their sophisticated data skills into the real world.
Book a call with one of our expert advisors today to learn how a Master of Data Science can expand your horizons within Data Science.
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