5 Reasons Why You Should Learn Machine Learning

Once confined to roles like data scientists and data analysts, Machine Learning (ML), a subset of artificial intelligence (AI), is now relevant to almost any IT job. ML decreases the software lifecycle and helps developers create more accurate applications. Additionally, ML reduces the time system administrators must repeat daily tasks and helps security professionals to detect malware.

While AI enables computers and machines to perform human-like tasks and simulate human behavior, ML focuses on algorithms to help a computer learn without the help of a programmer telling it what to do. Essential ML and AI skills are needed for the IT job you hold today and the one you’ll hold in the future.

The most in-demand jobs, according to The Future of Jobs Report from the World Economic Forum, include Data Analysts and Data Scientists, AI and ML Specialists, Big Data Specialists, Software Developers, Information Security Analysts, and Digital Transformation Specialists. And which of those jobs use ML? All of them.

Below are five reasons why you should learn machine learning:

1. Companies Are Looking to Hire for Key Machine Learning Roles 

Businesses have already implemented machine learning for testing software, fixing bugs humans tend to overlook, and rapid prototyping. From 2017 to 2022, the number of AI capabilities used in organizations doubled, according to the McKinsey report, The State of AI in 2022.

The report also identified a growing problem: Hiring for critical roles like machine learning engineer requires much work. The report reveals that 28% of respondents found it “very difficult” to hire machine learning engineers in 2022, and 42% found it “somewhat difficult.” Approximately 20% of respondents found it “much more difficult” to employ machine learning engineers compared to the three previous years, and 28% found it “somewhat more difficult”

This workforce gap means there are opportunities for IT professionals to advance their skills and careers by learning ML. These findings also suggest that people already working in organizations would benefit from being trained to fill needed ML engineer roles.

2. Companies Are Looking for More Specialized Solutions to Their Problems

Suppose you have adopted a public cloud provider like Amazon Web Services (AWS). In that case, you already have access to several high-level Machine Learning services that you can quickly implement in your organization. But eventually, you’ll encounter a problem that your current vendor cannot solve. 

You’ll want a specialized solution to serve your employees or your customers. For example, a healthcare company might use image recognition from its cloud vendor to assist with injury or disease diagnoses. Over time, they might want to pair image recognition with data from wearables and other technology to predict a patient’s risk for illness. In this case, they need employees with machine learning skills to build the specific solution they desire.

Think of building a specialized machine learning solution like building a house. A cloud vendor has the bricks, but you still need someone who understands construction to take the bricks from a pile in your yard to a palace that wows your neighbors. 

When it’s time to build a specialized solution for your company, employees need to understand the services offered by their cloud vendor and the basic mathematical concepts behind machine learning. Otherwise, the machine learning solution may not function properly.

3. IT Professionals are Needed to Complement Automated Machine Learning

If your job focuses on writing code, you have some competition: Robots. No-code and low-code tools can now write code and handle low-development tasks. AI coding tools can help developers with repetitive tasks like quality assurance, and the coding tools can improve over time through machine learning.

Machine learning methods have helped AI coding capabilities improve at a rapid rate. In a coding competition with more than 5,000 participants, an AI system called AlphaCode outperformed 45.7% of programmers, according to Science.org. DeepMind, a subsidiary of Google’s parent company, Alphabet, reported that the system could solve 34% of assigned problems as of December 2022, and often used creative approaches to do so. However, researchers say that AlphaCode still makes some mistakes, highlighting the need for programmers who can work alongside AI coding systems to improve the systems’ capabilities and build complex projects.

To complement the current capabilities of machine learning services, developers will need to learn particular skills, such as Python, the language of choice for machine learning. Developers will also need to understand the concepts behind machine learning to understand how to develop or modify algorithms and optimize overall performance.

4. You May Need to Work With ML Models

Machine learning can compete with humans, but it won’t remove them from work processes completely. Instead, your role will evolve, and your daily tasks will change. For example, ML can help with image recognition. An IT professional who previously spent time categorizing images might see their responsibilities shift from labeling images to teaching the ML algorithm to categorize images.

There are a few approaches to ML, including supervised learning, unsupervised learning, and reinforcement learning. These approaches are used to train the ML model and require humans to provide information to help the ML model learn to make decisions or predictions. Eventually, the model learns to spot patterns between the data it receives and the output labels. Once the model learns these patterns, it can produce accurate results when it receives new data.

The supervised learning approach requires labeled datasets, which means the dataset includes some additional information like a target variable or output variable. For instance, if you were trying to teach an ML model to recognize the images of different animals, you might share a dataset of additional images with a label categorizing each animal.

Unsupervised learning uses unlabeled datasets, meaning it receives datasets without additional information. If you were using an unsupervised learning approach to teach an ML model to identify the images of different animals, you might share a dataset of images without any labels. The ML model would likely group the animals based on identifiable characteristics like color or size.

The reinforcement learning approach means you train the ML model through trial and error. For example, if you shared images of different animals with the model, it would randomly guess the animal’s name. If the model is correct, it gets a reward. If the model is incorrect, it receives a punishment. This reinforcement helps the ML model learn over time.

Humans must monitor the machine learning models and make judgments that apply to real-life scenarios. For example, many companies in the financial services industry use machine learning to identify anomalies in their data. These companies use applications built with machine learning to identify duplicate payments and other fraudulent activities. The applications can complete this task quicker than humans can, but a human is still needed to ensure the application complies with relevant regulations and test the application’s performance to make sure it is accurate, among other tasks.

5. Machine Learning is Becoming a Crucial Skill Across Industries

Machine learning is here to stay — and it already affects your industry. The demand for AI/ML has grown 370% over the last five years and tops the list of emerging skill sets in 2023, ahead of cloud computing, according to the State of Skills report from Burning Glass, Business-Higher Education Forum, and Wiley.

The need for IT professionals with AI and ML skills will continue for some time. By 2025, according to The Future of Jobs Report, 93% of companies surveyed indicated they are likely to include AI in their growth strategy. And 95% of companies in the Digital Communications and Information Technology sector, 90% of financial services companies, and 89% of health and healthcare companies all plan to use AI.

Conclusion

Embracing machine learning will make you more competitive in the job market and help you develop specialized solutions for your company. From providing insight into customer data to assisting with fraud detection, your organization probably already uses ML. Learning the basics of machine learning is the first step to unlocking its capabilities. No matter whether you’re a beginner or an advanced data analyst, there’s an ML course that’s right for you. Check out our machine learning courses.