What is machine learning?
Machine learning is a method of data analysis that uses algorithms to imitate the way humans learn. In essence, machine learning is the process of systems learning from data to identify patterns and make decisions without human involvement. Although machine learning has existed for decades, the computing technology to effectively utilize machine learning for huge amounts of data has only recently become available. There are three prominent types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.
How does machine learning work?
A simple example is how we would teach a machine to identify a photo of a cat. The machine is given millions of images correctly labelled as cats and dogs to process. After it has finished processing the data set, the machine is given a completely new photo of an animal to identify - in our example, a cat. The machine makes a guess about what it believes it is seeing with degrees of certainty. So in response, it might return 98% confidence the image is a cat, and 2% confidence the image is a dog. This is known as supervised learning.
Unsupervised learning is the process of giving the machine data that is not labelled. The algorithm would then identify patterns in the data and group similar types into clusters. Unsupervised learning is useful when there is no data available for a desired outcome. For example, recommendations on platforms such as Netflix and Spotify use an unsupervised machine learning algorithm to suggest media based on what has been engaged with previously.
The third type of machine learning is known as reinforcement learning. Reinforcement learning uses a punishment and reward system to achieve an outcome. A fun example of this is teaching a machine to play a game, such as Snake or Super Mario. The desired outcome in these examples is to gain maximum reward either by scoring the most points or completing a level. Let’s consider how we would teach a machine to learn to play Snake. To start, the machine has no idea what to do and will perform a series of random actions to collect information about the environment (the game), and the agent (the player controlled snake). Over time, the machine learns what causes a punishment (bumping into a wall or itself) and what leads to reward (eating the fruit). It uses this information to successfully avoid punishment and receive the maximum award.
How will machine learning impact career-advancement?
Imagine a world in which a user visits a website, enters a topic of interest alongside their current abilities, and the system returns a pathway that guarantees a certain employment or promotion outcome. This would be incredibly helpful to young learners entering the workforce and individuals looking to change careers, gain specialist knowledge, or move to the next stage in their career.
The modern world of work is constantly evolving and segmenting. Take Marketing as an example. Once organizations would have hired a Marketer, but now they hire Social Media Managers, SEO Specialists, Paid Media Experts, and so on. Although the number of available careers is continuously growing, entry into these jobs isn’t always clear to job-seekers and often a degree is not enough. A tool that is easily accessible and provides clear, impartial instruction on what to study next or where additional training is required will remove the guesswork in career development.
Although there are services available that provide insight into career-advancement opportunities, humans aren’t able to assess the amount of data required to guarantee outcomes. These services are also often maintained by third-parties with an interest in directing learners or job-seekers onto their own courses or training programs.
Digital credentials, machine learning, and career-advancement
Data is core to machine learning and without sufficient data, algorithms aren’t able to be trained for classification purposes or easily identify patterns. Digital credentials are the ideal dataset for utilizing machine learning as part of career-advancement. As more digital credentials become available including micro-credentials and certified learning pathways, the data becomes richer and it is easier to identify patterns and trends.
Accredible’s Job Market Insights tool utilizes machine learning and statistical insights to support issuers in communicating career-growth potential. Issuers use Job Market Insights to showcase the value of their credentials to candidates including prospective salaries and job opportunities. This helps learners remove the guesswork of career-advancement. It can also contribute to closing the skill gap by encouraging talent pipelines and partnerships between digital credential issuers and employers.
The ability to guarantee employment and promotion outcomes is a part of our near future and we are excited for the opportunities it will bring. Through the utilization of technologies such as digital credentials, machine learning and AI, we will be able to say with certainty which paths to take to ensure career-advancement. This advice will be tailored to the individual, regardless of geography, economic sector, or level of education.
At Accredible, our vision is to increase society’s ability to educate and hire based on skill and to increase equity of opportunity. We believe in a future where individuals are confident in their educational ROI and everyone has the ability to succeed in their preferred career.
To learn more about digital credentials, Job Market Insights, or to start issuing digital credentials for training programs or learning pathways, contact the Accredible team today.
Download our guide to Bridging the Gap Between Education and Employment. The guide demonstrates how issuers can use digital credentials to empower candidates to pursue career-advancing opportunities. Digital credentials help candidates improve their employability and promotion prospects, and contribute to closing the skills gap.
Use this guide to:
• Make issued digital credentials stand out from competitors
• Support candidates to find employment and promotion opportunities
• Create targeted offerings that are more attractive to candidates
• Build, maintain, and improve partnerships with employers