Programming is how we’ve been building computer programs for over a century. But in the last few decades, machine learning has been rapidly taking over the world. Even though they’ve become an integral part of our daily lives, many of us don’t fully understand the difference between traditional programming and machine learning.
The difference between normal programming and machine learning is that programming aims to answer a problem using a predefined set of rules or logic. In contrast, machine learning seeks to construct a model or logic for the problem by analyzing its input data and answers.
Perhaps you’re still not sure what the difference really is—I don’t blame you. Read on to learn all about programming and machine learning in layman terms, from technical differences to real-life examples and career opportunities.
Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!
Table of Contents
What Is Programming?
We can explain the difference between machine learning and traditional programming on more than one level. This article assumes that you know nothing about programming at all. So let’s first understand what programming means in simple terms.
You see, 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.
Example of Programming
Say I have 100 pictures, and I want to filter all photos of a guy named Alex. I can do this by writing a set of conditions that will look for things like glasses, skin color, or other facial characteristics. These will be the rules or logic or my computer program. For example:
- If the current picture has a face, proceed to step 2. Else, move on to the next one.
- Is the face white? If yes, proceed to step 3. If not, move on to the next picture.
- Does it have glasses on? If yes, proceed to step 4. If not, move on to the next image.
- Does it have black hair? If yes, proceed to step 5. If not, move on to the next picture.
- Are the eyes brown? If yes, it is Alex. If not, move on to the next image.
There can be many more conditions, but you get the idea. The computer performs operations on the input data (100 photos) using my given rules (facial characteristics). It then successfully outputs the resulting data (Alex’s pictures).
This is the way traditional programming works. You give it input along with some rules, and it provides you with the output data.
Other examples of the software that runs on normal programming include operating systems like windows, web browsers like Chrome, video games, Microsoft Office, and many other programs we use every day.
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.
Example of Machine Learning
Let’s continue the previous example where we had to filter Alex’s photos from a set of 100 images. If we want to achieve the same task with machine learning, the first step would be to give it different photos of Alex so that the computer may learn what he looks like. These pictures are the training data (as our model gets trained with this dataset), and the rules used to identify Alex are called ‘feature sets.’ So in our oversimplified example, the computer will learn that Alex:
- Wears rectangular glasses
- Has short black hair
- Has a long white face
- Has big brown eyes
The first step is to feed Alex’s photos to the computer, making sure that they include and highlight the above features. After that, the machine would be able to recognize and filter Alex’s pictures on its own.
Like computer programs, machine learning algorithms are everywhere around us. Alexa, Siri, and Google Now are popular virtual personal assistance powered by ML. Traffic predictions, video surveillance, social media services, email spam filtering, virtual customer support, search engine results ranking, and product recommendations are examples of machine learning we use every day.
By now, you probably have an idea of how machine learning and traditional programming differ. But let’s discuss this distinction in detail in slightly more technical terms.
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.
Note: You may still be confused between ML job titles like ML researcher/scientist, machine learning engineer, data engineer, data scientist, etc. However, this article only aims to provide an overview of machine learning with respect to traditional programming. So we won’t be covering those topics here.
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:
- 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.
- 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.
- 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.
What Are Deep Learning and Artificial Intelligence?
We’ve talked a lot about machine learning and conventional programming. But any discussion about ML is incomplete without discussing two other terms closely related to ML: deep learning and artificial intelligence. Understanding these topics will help you appreciate how ML works and why it’s impressive.
Deep Learning
Deep learning is a machine learning technique that aims to teach computers to learn from and recognize examples. It is a crucial technology behind self-driving cars because it has enabled four-wheelers to identify people, lamp posts, and even stop signs.
The terms machine learning and deep learning are often confused with one another, so let’s understand the difference between them with a short example.
If we want to teach a computer to classify dogs and cats’ images with machine learning, we would have to manually select relevant features that can be used to identify the animal. The ML model will be trained using this data, and it will then reference those features classifying new images. Here’s the machine learning workflow:
- Start with an image.
- Extract its relevant features.
- Build a model that predicts the animal using the features.
On the other hand, deep learning does not require you to manually select features from images. All you have to do is feed lots of pictures directly into the deep learning algorithm, which then describes or predicts the animal. Cool, right?
Deep learning is often more complicated than machine learning. It also requires a lot more input data for training the model and powerful hardware so that it takes less time for the computer to analyze training data.
The best thing about deep learning is that it is highly accurate. Humans don’t have to select the best features representing the object; the machine itself does it. Sometimes this state-of-the-art technology of machine learning also exceeds human-level performance.
Artificial Intelligence
Artificial intelligence is a buzzword these days, and nearly every start-up claims to use the technology. It’s so popular that companies are even cashing in on the hype. Statistics claim that 40 percent of ‘AI startups’ in Europe don’t actually use AI. But what has artificial intelligence got to do with machine learning?
Artificial intelligence is a branch of computer science concerned with teaching computers how to think and act like humans. It is an umbrella term involving a broad set of techniques.
For example, robotics is the intersection of technology, science, and engineering. It produces robots that replicate human actions. In 2005, 90% of all robots could be seen assembling cars in automotive factories. That number has now come down to 50% as different industries find innovative ways to incorporate robots.
Natural Language Processing is another exciting field of AI. It is the processing of human languages by machine programs. Notice how your email provider can filter out some emails as spam or junk just by reading the subject line or text of the email? That’s NLP in action.
However, arguably the most commonly known branch of AI is machine learning. Every time you see a company use the phrase “Artificial intelligence,” they’re claiming that they’ve deployed machine learning algorithms and techniques to deliver the best services to their customers.
So to put things into perspective:
- Artificial intelligence is the umbrella term for teaching machines to behave like humans.
- Machine learning is a subset of artificial intelligence.
- Advanced machine learning is known as deep learning. It is a subset of ML.
Author’s Recommendations: Top Data Science Resources To Consider
Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.
- DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
- IBM Data Science Professional Certificate: If you are looking for a data science credential that has strong industry recognition but does not involve too heavy of an effort: Click Here To Enroll Into The IBM Data Science Professional Certificate Program Today! (To learn more: Check out my full review of this certificate program here)
- MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
- Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.
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.
As far as career is concerned, both these fields are attractive choices, and you can pick any of them depending on your predisposition. If you love building stuff and have excellent programming knowledge, go for software engineering. Or if you’re into mathematics, logic, and abstraction, machine learning will best suit you.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.
Affiliate Disclosure: We participate in several affiliate programs and may be compensated if you make a purchase using our referral link, at no additional cost to you. You can, however, trust the integrity of our recommendation. Affiliate programs exist even for products that we are not recommending. We only choose to recommend you the products that we actually believe in.
