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Machine Learning vs AI vs Data Science: How to choose your career path

THE DIFFERENCE BETWEEN MACHINE LEARNING, AI AND DATA SCIENCE

Advancements in new technology are being made every day, all of which will play a key role in shaping society as we know it. Learn about the difference between machine learning, artificial intelligence and data science.

At Monash University, you can specialise in AI (which includes ML) or data science. We offer the following courses:

To provide some clarity on these three often overused buzzwords, let us give you some short explanations of what machine learning, AI and data science actually mean.

What is machine learning (ML)?

Machine learning is a method of teaching software to learn from data and make decisions on their own, without being explicitly programmed to perform those tasks or make those decisions. It involves using large amounts of data and algorithms that can identify patterns in the data and make decisions based on those patterns.

The three primary areas of machine learning are supervised learning, unsupervised learning, and reinforcement learning.

The three types of machine learning

  1. In supervised learning, an algorithm is trained on labelled data, which means that the data includes both input data and the corresponding correct output. For example, a supervised learning algorithm for image classification might be trained on a dataset of images and their corresponding labels (e.g., ‘cat’, ‘dog’ etc.). The goal is for the algorithm to make predictions for new, unseen images based on the patterns it learned in the training data. Common applications of supervised learning include image and speech recognition, natural language processing and fraud detection.
  1. In unsupervised learning, the algorithm is not given any labelled training data and must find patterns and relationships in the data on its own. Clustering is a common application of unsupervised learning. Here, the goal is to group similar data points. Email marketing is a good example where companies use clustering to find which of their newsletter recipients are similar in terms of interests, opening times and days. That way, companies can send optimised newsletters to different clusters of users.
  1. In reinforcement learning, an algorithm learns by interacting with its environment and receiving rewards or punishments for certain actions. The goal is for the algorithm to learn to take the actions that will maximise the reward over time. Reinforcement learning is used in a variety of applications, including artificial intelligence, economics and neuroscience.

We use machine learning every day without even realising it. If you’ve ever searched for an image in your Google Photos gallery, you’ve seen the results of supervised machine learning in action.

What is artificial intelligence (AI)?

Even though artificial intelligence and machine learning are closely related, they are not the same thing.

Artificial intelligence (AI) is the ability of a machine/software to think like a human and perform tasks that would normally require human-like intelligence. You develop algorithms and systems that can analyse, process and interpret data to perform tasks such as problem-solving, learning and decision-making. AI can be divided into two categories: narrow AI and general AI.

Narrow AI refers to systems that are designed to perform specific tasks, such as image recognition, language translation or driving a car. These systems are trained to perform these tasks by being fed large amounts of data. They can then make decisions or predictions based on that data. However, they are unable to perform tasks outside their specific domain. Alexa or Google Assistant are some of the most popular examples of narrow AI in use today.

General AI, also known as strong AI or full AI, refers to systems that can perform a wide range of tasks, just like a human being. These systems can learn and adapt to new situations, rather than being specifically trained to perform a single task. General AI is still a topic of research and is not yet a major player in the field.

What is data science?

In the field of data science, you use algorithms, statistics, processes, scientific methods and systems to extract insights and knowledge from structured and unstructured data. It involves the collection, cleaning and analysis of data, as well as the development of models and visualisations to communicate the results.

Data scientists use a variety of tools and techniques from fields such as mathematics, statistics, computer science and domain-specific expertise to analyse and interpret data. They work on a variety of tasks, including predicting outcomes, finding patterns, identifying trends and generating insights.

The goal of data science is to extract useful knowledge from data and use it to make informed decisions. For example, streaming services use data science to establish viewing patterns not just for one user, but for all their millions of subscribers to make informed decisions about future series.

Skills needed for machine learning, AI and data science jobs

Even though data science, artificial intelligence and machine learning are all related fields that involve the use of data and statistical techniques, there are some variations in the skills you need for each.

Data science typically involves the use of statistical and machine-learning techniques to extract knowledge and insights from data. As a data scientist, you must be proficient in programming languages such as Python or R, as well as SQL and NoSQL databases. You should also be familiar with statistical analysis and machine learning techniques and should have strong problem-solving and communication skills. If you think this field is right for you, here are even more reasons why should you choose a career in data science and everything you need to know about data science jobs and careers.

In AI roles, you will be building intelligent systems that can perform a wide range of tasks, such as image or speech recognition, natural language processing and decision-making. As an AI researcher or professional, you need a strong background in computer science and mathematics, as well as programming skills in languages such as Python or C++. You should also be familiar with machine learning techniques and can think creatively and abstractly.

Machine learning jobs require you to train models on a dataset to make predictions or decisions based on patterns in the data. As a machine learning engineer/researcher, you should have strong programming skills and be familiar with mathematical concepts such as probability and linear algebra. You should also have experience with machine learning frameworks and libraries, such as TensorFlow or scikit-learn.

As you can see, data science, AI and ML all require strong programming and mathematical skills, but the types of skills you need can vary depending on the specific field and the tasks you perform.

Be at the forefront of technological change with Monash Online

If you’re still unsure what specialisation is the right one for you, here is an overview of all our IT online courses. Monash Online’s computer science and information systems courses prioritise student employability outcomes above all else and are ranked sixth in Australia by the Academic Ranking of World Universities (ARWU). More importantly, you’ll acquire the skills and confidence you need to make a positive change, whether it’s in your local community or at an international level.

Learn more about studying AI or data science online at Monash by calling a course consultant today on 1300 272 509 or arrange an online booking.

I am currently in a career transition phase, working to become a full-time software engineer; all this made possible by the excellent pathways Monash has created.

Rashid Elhouli, Monash Online Graduate
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