Artificial intelligence (AI) is a growth industry with early mover advantages. As AI use expands, expertise in the field will bring career opportunities. Build professional knowledge and skills with an AI course online. As well as free courses, you can study for a graduate certificate or master's degree in AI.
Free artificial intelligence courses are available online from the United States and many other countries. These are suitable for beginners as well as university graduates. Free online courses are especially good if you (a) are new to the AI field or (b) want to fill specific knowledge gaps.
Relying on free AI courses to build professional expertise is a challenge though. You have a lack of structure when doing free short courses compared to a full university program. As well, many students find it hard to stay motivated when (i) you haven't spent money on the course and (ii) don't receive an accredited degree, diploma or certificate at the end.
Learn with Google AI
Google Education offers a large set of free resources for anyone – at almost any learning stage – to explore AI and Machine Learning. As well as course, there are guides, podcasts, interactive content and videos. Google obviously knows a thing or two about data science, machine learning and artificial intelligence. The online programs are short courses, including many in the range of one to four hours long. Don't expect to become an expert just through Google Education. But you can gain a solid grounding in many areas of interest.
Coursera Artificial Intelligence
The Coursera platforms offers a large set of free courses in artificial intelligence from leading universities and companies, such as Stanford University and IBM. Don't be put off by the impressive provider names though. Many of the courses are introductory and suitable for beginners. The length of a course is usually measured in hours. Note that a small number of hours can translate into quite a few weeks if you're only able to do 2-4 hours a week for example. Popular courses include Machine Learning, AI for Everyone, Key Technologies for Business, Deep Learning, and IBM Applied AI.
If you want to learn how to deploy AI without learning advanced coding, an AI management course may be ideal. You gain essential technical knowledge on data handling, machine learning and artificial intelligence. Importantly, you also learn how to identify and capitalise on opportunities to use AI in a business setting. The shortest university-accredited AI management course in Australia is a Graduate Certificate in Artificial Intelligence Management, taking 8 months of part-time study online.
UTS Online Graduate Certificate in AI Management
The UTS Online Graduate Certificate in Artificial Intelligence Management is for tech-driven individuals who want to develop their knowledge in artificial intelligence, and data and process management. Students build on existing technical knowledge by learning how to develop and lead AI solutions to solve complex problems in organisations. You’ll learn the foundations of AI management, including the application of multi-dimensional visualisation techniques, machine learning methods and state-of-the-art algorithms, while applying knowledge of data ethics and regulation to achieve artificial intelligence solutions. The course is delivered part-time and 100% online by UTS, ranked 10th globally for artificial intelligence research (AI Research Index Issue Report, 2020).
You can study AI as part of a technology management program. In an online master or executive masters course, AI topics form part of a suite of technology subjects that are relevant to modern businesses. Flexible programs allow you to choose electives to meet your learning goals.
UTS Online Master of Technology Management
The UTS Online Master of Technology Management is for forward-looking professionals. Build analytical, strategic and leadership skills to manage technology-orientated activities. In the Artificial Intelligence specialisation, you study data visualisation and visual analytics, machine learning, artificial intelligence for enterprises, and data ethics and regulation. What you study in the rest of the technology program is up to you, with a wide range of electives available in fields such as digital strategy, cybersecurity management, and leading organisational change. The 100% online course can be completed while you work full-time.
UTS Online Executive Master of Technology Management
The UTS Online Executive Master of Technology Management is for experienced professionals interested in gaining new knowledge and skills. The course provides an accelerated pathway if you have a technical degree and relevant work history. Gain an executive masters by doing 8 subjects, compared to the normal 12 for an Australian master's degree. A large number of technology management subjects are available as electives, including in artificial intelligence and machine learning. The program allows you to work full-time while developing technology management skills.
In an AI course, you can expect to study machine learning and, in particular, the algorithms that can be used to generate artificial intelligence. Important related subjects are data analytics, data visualisation, and practical applications of machine learning.
UTS Online Graduate Certificate in AI Management
Artificial intelligence, machine learning, data visualisation, and databases are all elements of the data analytics field. But central to data analysis are the algorithms themselves. In this subject, you learn how basic, more powerful and subtle algorithms work. We take a research-inspired approach so that you learn to apply state-of-the-art algorithms to your professional practice. Applications such as social network analysis and text mining are considered.
In this subject, you work on practical problems using data analytics, data mining and knowledge discovery methods. In particular, artificial intelligence students will learn how to conduct data mining research and development projects. Issues of ethics, privacy and cultural limitations of AI solutions are explored with the help of case studies.
In this subject, you'll learn to use visual analysis software and the practice of cutting-edge data visualisation. Students explore the procedure (loop) and visual data analytics methods. You'll also consider human involvement (or input) into the loop of analytical reasoning that is facilitated by interactive visual interfaces.
In this course, students explore regulation and ethics of data practices in the digital environment. You'll build a deeper awareness of the moral and ethical foundations of privacy, security and accountability practices. Topics covered include regulation of data collection activities, algorithmic accountability, and biases associated with data analytics tools.
After completing an AI course, you should feel comfortable in using multiple machine learning methods. You should also have a solid grasp of when and how AI solutions can be used to generate business value. Common learning outcomes include being able to do the following.
- Identify and contrast the technologies used to achieve AI solutions.
- Explain the scope, limitations and application of several machine learning methods.
- Apply machine learning methods.
- Use data visualisation to illustrate and navigate large information spaces.
Many useful artificial intelligence courses don't have "AI" or "artificial intelligence" in the title. That's because AI is related to data science, machine learning, natural language processing and other concepts. To navigate your way around the options to learn artificial intelligence, here's a quick explanation of terms.
Machine learning is a sub-field of AI with a narrower scope but which is important to support AI. Machine learning refers to automated analytical model building. Using provided examples, the automated process finds insights within data without being explicitly told what to do. ML methods include neural networks and statistics.
Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems. - SAS
While the terms AI and Machine Learning are often used interchangeably, they are different. AI refers to a less clearly defined process in which machines mimic intelligent human-to-human interactions.
Cognitive computing is where computer models simulate human thought processes. The method is especially useful in complex situations where multiple answers or interpretations may be possible. Applications include face detection, sentiment analysis and risk assessment.
Computers struggle with some tasks that people are naturally good at, such as understanding language and identifying objects in images. Cognitive computing attempts, in some ways, to mimic how the human brain processes information.
Cognitive computing makes use of artificial intelligence (AI) technologies, including neural networks, natural language processing, machine learning, speech recognition, and object recognition.
Data science is a broad field that you could consider encompasses both AI and machine learning. Data science is an interdisciplinary field that uses mathematical and programming techniques to extract knowledge and insights from big data, which may start out as noisy and/or unstructured.
Data scientists use their techniques and skills to contribute to AI development. They write algorithms for learning patterns and correlations in data. AI can use these to produce predictive models that then generate insight from data. Data scientists also create and use AI as one of their tools for understanding big data and informing decision-making.
Deep learning is a sub-field of machine learning. Deep learning takes its inspiration from the human brain and relies on artificial neural networks with three or more layers.
Completing an AI course online could easily set your career off into a different and exciting direction. Artificial intelligence is here to stay and job opportunities are growing.
Job titles for professionals with AI skills include data scientist, senior data scientist, data scientist (A.I.), machine learning engineer, machine learning head, artificial intelligence (AI) lead, artificial intelligence engineer, senior software developer, and R&D algorithms engineer. AI professionals often have a computer science bachelor degree (or similar) as well as holding postgraduate qualifications and/or other advanced training.
Knowing how AI works, along with potential uses and limitations, can also be beneficial in numerous careers beyond the development roles mentioned. Knowing AI will help you make better use of artificial intelligence technology and be a fast adopter. In healthcare, information technology, entertainment, communications, digital marketing, social media and many other industries, AI insight promises future career advantages.
For a free artificial intelligence course, anyone can choose to enrol. There are no entry requirements as such. However, knowledge of computer science and programming languages may be beneficial in many cases and, for advanced courses, often essential.
Especially for courses in AI above beginner level, it is recommended you check out the "background knowledge" or similar requirements. The provider may explain what pre-existing knowledge you need for the course to be worthwhile or what introductory courses you should do first.
Entry requirements for graduate certificates, graduate diplomas and masters in AI reflect that these are postgraduate courses of a technical nature. Admission requirements are, in general, that you have a relevant degree and professional experience. Entry into graduate certificate courses may be more relaxed. For the UTS Online Graduate Certificate in AI Management, you need an IT-related bachelor degree (or higher qualification) AND a year of experience in a role related to the course.