Machine Learning in Data Science
With the rise of artificial intelligence, machine learning has become an integral component of data science. Encompassing the scientific study of algorithms and statistical models, machine learning applications give computer systems the ability to perform specific tasks without relying on explicit instructions.
In the annual Data Science and Machine Learning Market Study by Dresner Advisory Services, data mining, advanced algorithms, and predictive analytics ranked among the highest-priority projects for enterprises adopting AI and machine learning in 2019. Now that these deep learning technologies are in such high demand, understanding the core principles of machine learning has never been more important for ambitious data scientists.
Monash Online’s Graduate Diploma of Data Science can help you capitalise on the opportunities being created by machine learning. Discover how this qualification will enhance your knowledge of the following machine learning principles and their applications in data science.
Used to make predictions and calculated suggestions informed by vast amounts of data, algorithms are a cornerstone of machine learning. Having been applied in mathematical formulas for centuries, the concept of the algorithm isn’t new. However, computers have taken the algorithm to new heights, so it can now be used to solve complex problems. Used to map out inputs, supervised algorithms and unsupervised algorithms are both essential to machine learning.
Many different variations of machine learning algorithms can be applied in data science, including:
- Linear regression
- Logistical regression
- Dimensionality reduction.
With a thorough understanding of algorithms and how they function in the context of machine learning, you’ll be able to turn data into valuable business insights. These insights can then be used to foster growth and innovation through data-driven initiatives, such as:
- Identifying channels with high conversion rates
- Optimising delivery logistics with location tracking
- Improving the effectiveness of targeted advertising
- Automating manufacturing processes.
It’s the role of a data scientist to turn data insights into tangible assets, which is why their roles are becoming increasingly important to businesses.
Programming languages teach computers how to think intelligently, making them an essential part of machine learning applications. Mastering the art of programming for machine learning will broaden your skillset significantly, increasing your chances of being considered for advancement opportunities in the data science field.
Having foundational knowledge of these languages will give you the ability to use them in a machine learning context, giving you greater control over your career trajectory.
An essential skill for data scientists, data modelling involves creating a descriptive diagram of information within a database. In the context of machine learning, data modelling is used to train algorithms to perform tasks autonomously. According to Towards Data Science, the main objective of a machine learning model should be to satisfy business needs.
With organisations becoming more reliant on vast amounts of data, modelling provides an effective guide for the design and implementation of complex databases. Since modelling is a core principle of machine learning, knowing how this process works will ensure you have the skills and knowledge to keep up with the emergence of new technologies.
Natural Language Processing
Natural language processing (NLP) is a branch of artificial intelligence concerning communications between humans and computers. From the evolution of Google Translate to the development of communication aids for people with disabilities, it can already be seen in many existing technologies.
Even though human language is extremely complex, giving it structure is still possible thanks to the combined disciplines of data science and machine learning. NLP allows for the interpretation of vast amounts of spoken and written communications such as phone calls and emails. Capturing this data helps businesses create NLP technologies that can be used to enhance operations. Some prominent examples of NLP include:
- Email filters - By identifying certain words or phrases, NLP can effectively filter spam emails
- Search results - Search engines rely on NLP to find the most relevant results based on user intent and past searches
- Order processing - NLP is now capable of answering phone calls and emails in order to take orders.
As a data scientist, these are the types of NLP technologies you’ll have the opportunity to specialise in.
Rather than watching the machine learning revolution unfold from a distance, furthering your education will help you align your skills and experience with its growing prevalence.