As more industries recognize the monetary value of machine learning, the global machine learning market is expected to grow at a rate of 38.8% CAGR, from $21.17 billion in 2022 to 209.91 billion by 2029. Such exponential growth will create a competitive job market. Recruiting top talent will become challenging, however, the right recruitment software will help recruiters and HR specialists identify the right candidates.
What is machine learning? Definition
Let us get started with the basics.
Machine learning is the study of artificial intelligence (AI) and computer science. Its focus is on using data and machine learning algorithms to imitate how humans learn and predict outcomes without being explicitly programmed to do so. In a nutshell, a machine learning model inputs historical data to predict new output values.
Artificial Intelligence (AI), on the other hand, is the process of using a complex set of machine learning algorithms, such as deep learning to think like humans and mimic human actions. Deep learning, as part of machine learning and artificial intelligence, is responsible for imitating how humans gain knowledge.
In other words, a Machine Learning algorithm analyses the data provided, and artificial intelligence is the mastermind that takes actions based on the data delivered.
How we use machine learning differs from business to business depending on the dataset involved and individual business needs.
What is machine learning used for?
The purpose of machine learning is for users to feed a computer algorithm with as much data as possible. Machine learning programs then analyze the data and make data-driven recommendations and decisions based on the information provided.
Through algorithms, machine learning allows developers to identify patterns in end-user data and create mathematical models based on pattern recognition. The gathered information is used to create and implement predictive applications in the machine learning system.
Nowadays, machine learning is embedded everywhere, for instance in::
– internet searches,
– email filters,
– website and purchase recommendations,
– banking software capable of detecting unusual transitions.
The scope of machine learning is expanding to now include neural networks, deep learning, and speech recognition software embedded in apps, phones, and smart speakers. Future developments could see people supported by readily available personal assistants, programmed with natural language processing, to help manage our everyday lives.
Machine learning is versatile and can be applied to several applications, like:
- Customer relationship management (CRM Software) – machine learning analyzes emails and prompts sales representatives to respond to the most urgent messages. This enables teams to manage multiple sales pipelines and automate repetitive processes.
- Business intelligence – machine learning is used to identify data points of value, pattern recognition of data points, and anomalies.
- Human resource information system – machine learning filters through applications and identifies the best candidates.
- Self-driving cars – machine learning embedded in semi-autonomous cars with partial object detection.
- Virtual assistants – smart assistants use supervised and unsupervised machine learning models to understand natural speech and supply context.
Examples of machine learning
When done right, machine learning can personalize consumer experience within your business. In the 21st century, personalization is the key, as recent consumer research “indicates 80% of consumers are more likely to make a purchase when brands offer personalized experiences”.
Think of Netflix as an example. Although many of us have Netflix, if you have not noticed, the front page and recommendations differ for every user. Not only this, but also the accompanying thumbnail to the same film or series.
Source – Netflixtechblog
Netflix, just like any other well-performing platform, collects heaps of user data, feeds it to machine learning, and uses artificial intelligence to make personalized human recommendations to each user.
Engineers employed by Netflix analyze viewer habits based on multiple factors. The recommendation system embedded in Netflix estimates the probability of a user watching a particular title based on several factors:
- Viewing history
- Category, year of release, genre
- What other viewers with similar preferences watch (and several others)
The machine learning techniques used by Netflix continue to learn from the users viewing habits. So, every time we watch a movie or a series, Netflix is collecting valuable input data, feeding it to the machine learning algorithm behind the scenes, and refreshing our recommendations based on data analysis. The more we engage with Netflix, the more up-to-date and accurate the algorithm, and our suggestions, are.
That is how as we get sucked into the world of Netflix, its personalized recommendation algorithm produces $1 billion a year in value from customer retention.
How to learn about machine learning?
As with any role, there is a particular skill set that recruiters or HR specialists will be looking for from data scientists.
The best place to start is with the fundamental concepts such as:
- Computer Science Basics
- Data Structure (binary trees, arrays, linked lists)
- Statistics and Probability (Bayes rule, Gaussian mixture models, and the Markov decision process)
- Programming Knowledge (variables, functions, data types, conditional statements, loops)
- Graphs
At the basic level, machine learning engineers should possess an excellent grasp of mathematics, statistics, and the ability to solve analytical problems.
In particular, HR Specialists and Recruiters can be on the lookout for machine learning engineers with an understanding of Matrices, Vectors, and Matrix Multiplication. Advanced machine learning roles also require the knowledge of robotics, AI, and deep learning.
A machine learning engineer works with classification algorithms or regression algorithms. The three main machine learning categories are: supervised learning, unsupervised learning, and reinforcement learning.
Candidates should also possess knowledge of various tools, techniques, and programming languages such as Python, R, Java, and C++.
What is a model in machine learning
A model in machine learning is a file designed to identify patterns or to make decisions from previously unseen datasets with minimal human intervention. A data scientist trains the machine learning model with a large dataset and optimizes the machine learning algorithms to identify patterns or outputs from the dataset.
Machine learning models
In terms of machine learning models, the majority of them are based on machine learning algorithms. Generally, they are classified as regression algorithms which fall under supervised machine learning, and unsupervised machine learning which are from clustered algorithms.
Supervised learning algorithms or supervised machine learning is used to classify data or make accurate predictions. Under the supervised learning algorithm, there is a need for human intervention to label, classify and enter the data in the algorithm.
Unsupervised learning algorithms or unsupervised learning, use machine learning algorithms to analyze and cluster unlabeled data sets. As the data does not have to be labelled, there is no need for human intervention.
Demand for machine learning specialists
Are machine learning specialists in high demand?
According to DevSkiller Top IT Skills Report 2022, within Data Science, machine learning came second place (24.04%) in terms of importance to business objectives. This trend is expected to continue to grow as more tech companies incorporate machine learning into their everyday processes.
In 2021 Data Science saw a 259% growth, becoming the fastest growing IT skills DevSkiller customers were testing. As companies recognize the true value of data, Data Scientists can help them make the most out of the information available.
In 2019, Indeed reported that the role of machine learning engineer saw a 344% growth in the number of postings, taking first place as the best job in the USA.
As every industry undergoes a digital transformation, computer and information technology roles will continue to be in high demand. The number of positions in this sector is predicted to grow by 11% from 2019 to 2029.
Download your free copy of the full DevSkiller IT skills report 2022
How to assess machine learning professionals for recruitment?
If you are a recruiter or HR specialist tasked with recruiting a machine learning engineer, there are a few things you need to know.
- As of 2022, there is a high demand for machine learning engineers and a talent gap due to a lack of experience. If you read this article, you should know that there are certain qualities and abilities, not to mention machine learning programs and techniques, data scientists need to know.
- This is still a relatively new field for recruiters and HR specialists. To recruit the best candidates, introduce a machine learning skills test into your recruitment process.
- Remember, technical assessment improves the chances of hiring skilled talent. Candidates that when recruited, can hit the ground running.
Machine learning skills test: which one to choose?
To assess potential candidates’ practical skills and their ability to work within a real work environment, a machine learning skills test should meet the following criteria:
- Demonstrate the quality of professional work
- Test duration (maximum time should be 1-2 hours)
- Easy to follow instructions
- Ability to match the difficulty level to the candidate’s abilities
- The solution should be quality checked and ensured it works in extreme situations
- Be representative of real work situation and provide candidates with all of the necessary resources
- Give candidates access to libraries, frameworks, and various tools they would usually have at their disposal
How to screen machine learning skills – Check it out now
Finding a reliable and accurate machine learning test doesn’t have to be challenging. For example, the RealLifeTesting™ methodology created by DevSkiller replicates the real-life work environment in which candidates will work.
RealLifeTesting™ examines the prospect’s ability to build applications or features, examining their practical skills. During the practical skills test, access to resources such as GitHub, Stack Overflow, and Google is permitted.
As a recruiter, you can send the test anywhere in the world and have the results generated automatically. Time is money, and with DevSkiller RealLifeTesting™ identifying a good machine learning candidate is easy.
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