What is The Difference Between Traditional Programming and Machine Learning?

We have been creating computer programs using programming for more than a century. However, machine learning has quickly taken over the world during the previous few decades. Many of us are unaware of the differences between traditional programming and machine learning, despite the fact that they have become an essential part of our daily lives.

Normal programming and machine learning are different in that normal programming seeks to solve a problem by using a specified set of rules or reasoning. Machine learning, in contrast, examines the input data and solutions to the problem in order to build a model or logic for it.

What Is Programming?

We can explain the difference between machine learning and traditional programming on more than one level. This blog assumes that you know nothing about programming at all. So let’s first understand what programming means in simple terms.

Computers are complex, powerful machines able to perform tasks beyond human ability. But as unique and remarkable as they are, computers are useless in themselves. They are complicated tools that have no intuition and can do nothing on their own. Computers need the help of humans to tell them what to do.

That’s what programming is: telling a computer what to do. We do this by writing a set of instructions in a language that the computer can understand. This set of instructions, or code, is known as a computer program or software, and the process of writing such code is called programming.

When you cook a recipe, you are the computer, while the recipe’s author is the programmer. The author gives you a set of instructions (program), and you read and follow them step by step.

Programming has been around for more than a century, and the earliest software dates back to the mid-1800s. Computer programs are all around us. From the movies we stream to the shopping we do, code makes it all possible.

What Is Machine Learning?

Telling computers what to do has turned out fantastic for us. We’ve built operating systems, websites, and loads of other computer programs that help us accomplish day-to-day tasks. But the thing with computers is that they have no intuition. We must explicitly tell them every single step; they cannot do anything on their own.

What if you could teach computers to develop a sort of intuition? How cool would it be if computers could learn from experience just like humans (assuming that humans do learn from their experience, even though there are plenty of historical examples that claim otherwise)? Imagine machines being able to improve over time, entirely on their own.

That’s exactly what machine learning is: teaching computers to learn and act as humans do. It means writing algorithms for computers to access data and use them to learn for themselves. The process involves giving the machine input data to learn from, as well as an algorithm to use. The computer then processes the algorithm and learns how to function on its own.

Remember, when you search for dogs on Google, it gives you relevant images? How does Google know what “dog” means? Well, Google’s computer first gets a large number of dog images so that it can learn what dogs look like. Then, it searches for patterns of colors and pixels that help it predict if a given image is a dog. This is machine learning in action.

The Difference Between Normal Programming and Machine Learning

Conventional programming is a manual process. It requires a programmer to create the rules or logic of the program. You have to manually come up with the rules and feed it to the computer alongside input data. The machine then processes the given data according to the coded rules and comes up with answers as output.

In traditional programming, you’re the one who creates the program, which then processes information according to the rules defined by you and outputs results.

On the other hand, machine learning is an automatic process. It only requires that you give it the input and output data. In machine learning, you don’t define the rules; you only feed input data and answers. The computer studies the provided information and comes up with a model or program to solve the problem. 

Let’s take a final example to see how powerful machine learning is. Say you want to create a program that detects a person’s activity (walking, running, jogging, or biking) from their speed. You’ll have difficulties solving this problem with the traditional approach because people walk, run, and bike at different speeds depending on their age, health, environment, etc.

However, suppose you chose machine learning to build the same problem. In that case, all you have to do is get tons of examples of people doing different activities along with their labels (i.e., the type of activity). The computer will then learn and create a model that can predict a person’s actions based on their speed.

Careers

Both machine learning and conventional programming offer many job opportunities for freshers and experts alike. As we’ve discussed, software engineering is about breaking down a problem, solving it, and composing a solution in a language that computers can understand. On the other hand, machine learning aims to teach the computer how to develop the solution independently by analyzing input data and answers.

Here, we’ll talk about the developer position for each of these methods: software developers and machine learning engineers. Let’s answer questions like which career is more attractive? Which position pays you more? Or which job will best suit you?

Software Engineer

The job of a conventional programmer (also known as software engineer or software developer) revolves around designing and developing applications software and computer system software. The programming field is snowballing because we’re becoming more reliant on technology day by day.

Software engineers have extensive knowledge of software development and programming languages. Their primary task is to create applications that allow people to do particular tasks on a computer or another device. Or they may develop the underlying system software that runs the machines.

According to the U.S. Bureau of Labor Statistics, software developers brought home an average of $107,510 in 2019. The top earners made more than $160,000, while the lowest rung made around $65,000. Employment for this position is projected to grow 22 percent from 2019 to 2029, much faster than the average 4% for all occupations.

To become a software developer, you’ll usually need a bachelor’s degree in software engineering, computer science, or a related field. You’ll also need to know a wide variety of technologies, especially if you want to be a full stack developer. However, breaking into the programming field isn’t difficult if you can acquire knowledge and skills.

Machine Learning Engineer

A machine learning engineer’s job is geared more toward manipulating datasets and applying ML algorithms to train computers. Probability, statistics, and lots of mathematics are part of an ML engineer’s day-to-day activities. Most of their day is spent applying various ML algorithms using libraries like Scikit-learn and TensorFlow.

According to the Bureau of Labor Statistics, computer and information research scientists (the category into which machine learning and AI jobs are included) earned $122,840 on average in 2019. The job market for machine learning engineering is projected to grow 15 percent from 2019 to 2029, much faster than average.

Education qualifications are stricter when it comes to becoming a machine learning engineer. A bachelor’s degree is standard, but many job postings require a master’s degree in statistics, mathematics, computer science, or a related field. If you want to work as an ML engineer, be prepared to learn advanced math and statistics concepts like linear algebra and calculus.

You don’t have to know many programming languages, though. Of course, experience with programming helps, but you don’t need to be a programming ninja. Python and R code are the most common programming languages used for machine learning purposes, so knowing these two will suffice. Apart from that, you’ll need to know how to extract data from databases using a language like SQL.

Which One Should You Pick?

Both of these careers are trending, offer an excellent salary, and promise strong employment growth. Since they’re both decent career choices, it can be difficult to pick between the two. We’ve already discussed their responsibilities, salary, and job outlook, but here’s what you need to consider when choosing the field for yourself:

• Do you have a background in statistics or mathematics? Or can you afford to get a master’s or Ph.D. in those subjects? If so, you may be more suited for machine learning. However, if you’re more into practical stuff, opt for programming.
• Programming is about building things. If you like to see your effort turn into a final product, software development is for you. On the other hand, machine learning should be your pick if you’re into logic and love solving complex numerical problems.
• Choose your profession based on your liking. Both of these fields offer an excellent salary and have a promising job outlook, so you don’t need to worry about money or security.

How Much Programming Knowledge Is Required for Machine Learning?

It depends on how you want to use machine learning. If you plan on solving real-life business problems by applying ML models, you’ll need to have some programming background. However, if you just want to study the concepts of ML, knowledge of maths and statistics will do.

As we’ve discussed, you won’t need to master a lot of programming to get started with a career in machine learning. You just have to know the fundamentals of programming, memory management, data structures, algorithms, and logic. Programming languages offer many handy ML libraries that make it easy for anyone who knows the basics to implement ML models.

A few programming languages are considered the most efficient for machine learning tasks. Python is the most common one among these. It has an extensive collection of in-built libraries and packages like Scikit-learn and TensorFlow. The language is also flexible, allowing you to approach ML problems in simple ways.

Other appropriate programming languages are R code, Java, Julia, and LISP. It would be best to learn at least two of these languages so that you’re better equipped for an ML engineer role. Python and R code are currently the most widely used languages in the field.

Will Machine Learning Replace Traditional Programming?

With all the developments in artificial intelligence, it’s natural to wonder if machine learning will completely replace the need for programmers in the future. Will we become totally dependent on machines? Are people going to lose their jobs? Will software engineers become a thing of the past?

The answer: not really. Although machine learning will transform the way we develop computer programs, it won’t eliminate the need for human coders. Many aspects of software development will be automated using ML and AI. But it doesn’t mean humans will no longer be required for programming any time soon.

Machine learning and artificial intelligence will complement mainstream programming techniques rather than replace them. For example, we can use machine learning to construct predictive algorithms for an online trading program. And at the same time, the platform’s UI and other components can be created using a standard programming language like Python or Ruby.

AI has already begun helping developers code computer programs. DeepCode is a semantic code analysis tool powered by AI, which is considered the Grammarly of code writing. Ulzard is another AI-powered tool that instantly transforms hand-drawn design mockups into HTML and CSS.

The role of programmers will undoubtedly change as AI systems improve further. Their responsibility will shift from writing code line-by-line to curating and analyzing input data for machine learning algorithms. This change is inevitable, and computer programmers will need to enhance their skills and focus on the least automatable ones.

As far as job opportunities are concerned, machine learning and AI reduce software development costs for companies. This means they can produce more software in less time. Business opportunities will undoubtedly skyrocket as the demand for software keeps increasing, and production costs are lowered. So rest assured that people likely won’t lose their jobs.

Why Is Machine Learning So Popular Now?

Machine learning is a hot topic, but it’s not a new one. ML and deep learning have been around since the 1950s. But if you look at Google trends, you’ll see that machine learning has been skyrocketing since 2014.

If it’s a 70-year-old technology, why is everybody talking about ML now? Well, there are three primary reasons why machine learning has become a buzzword nowadays:

1. The machine learning field has matured. We’ve developed powerful ML techniques in the last few decades. The tools that apply these techniques, like the Weka framework, have also evolved over the last 10 to 20 years. Hence, the field of machine learning has changed a lot. 
2. There’s an abundance of data. The data we collect and store is growing rapidly. Statistics claim that 2.5 quintillion bytes or 2,500,000 Terabytes of data are created every single day. Most of this data has been generated in the last few years. So we have more than enough data to train machine learning models.
3. Advanced hardware is available. We now have extremely powerful computers at our fingertips. One can rent these computation beasts at cents to a few dollars per hour and run massive experiments on large datasets. Advanced GPUs with thousands of cores are more than able to perform machine learning operations.

Conclusion

Machine learning and traditional programming are ways to create software programs that solve specific problems. Both of them have their uses and can work together to build remarkable technology. The difference between them lies in how they approach problems. In machine learning, you only supply input data and answers, and the computer figures out a model for solving similar problems in the future. However, in normal programming, you have to give the computer step-by-step written instructions for solving a problem. In other words, you have to provide it with the model or logic.