In programming, we can segregate the world into two broad categories – Conventional programming and Machine learning. Conventional or conventional programming has been around here for more than a century. The first computer programming was introduced in the mid-1800s. On the other side, machine learning programming is a new addition to this family, which has revolutionized the business for the last few decades. We see its primary uses in artificial intelligence and embedded analytics. This is a more advanced type of programming while quite different from conventional programming.
Machine learning is also known as augmented analytics. In conventional programming, programs are created manually by providing input data and based on the programming logic, and the computer generates the output. On the contrary, in machine learning programming, the input and output data are fed to the algorithm, creating the program. The powerful insights come out as a result that predicts future outcomes. Not only by definition but also by nature, these two programming languages are different. In this blog, we will see how machine learning is different from normal software development. Let’s start with the description.
What is conventional programming?
Conventional Programming uses conventional procedural language. It could be assembly language or a high-level language such as C, C++, Java, JavaScript, Python, etc. Conventional programming is a manual process, which means the programmer creates the logic of the program. They need to code the rules and write lines of code manually. They provide the input data, and based on the program’s programming logic; it produces the desired output. The conventional programming approach is algorithm dependent, and for a program, multiple algorithms can work. It is up to the programmer how he will design and develop the logic of the program.
What is machine learning?
Machine learning comes under artificial intelligence. Artificial intelligence is an umbrella term that contains many realms like machine learning, image processing, neural networks, cognitive science, and many more. Unlike conventional programming, in machine learning language, the computer uses a pre-written algorithm and learns how to solve the problem itself. It is a more sophisticated way of solving a problem. Machine learning language is beyond algorithmic solutions; instead, it trains a machine to solve different complex tasks by itself.
Machine learning (ML) in the field of the scientific study of algorithms and uses various statistical models. The computer systems use these statistical models to perform a specific task effectively. Here you don’t need to provide explicit instructions; instead, it relies on patterns and inference. As shown in the above image, machine learning is a subset of artificial intelligence. Machine learning language algorithms build a mathematical model depending on sample data. This data is known as “training data.” Using these data and algorithms, prepares predictions or decisions. Here you don’t need to program to perform the task explicitly.
How is machine learning different from normal software development
In conventional programming, a programmer needs to hard code the logic of the program. In machine learning, it depends a lot on the machine which learns from input data.
Conventional Programming is not a very advanced level, where decision making is based on IF-ELSE conditions. Therefore, many solutions cannot be modeled with it. One of the main reasons behind this is the variation of the input data variable, which increases the problem’s complexity. On the contrary, machine learning programming solves the problem by modeling the data with train data and test data. Based on these data and statistical models, machine learning predicts the result.
So, we can say, conventional programming and machine learning programming are not interchangeable terms. A data engineer can’t replace the work of a conventional programmer and vice versa. However, every data engineer must know at least one coding language. But a conventional programmer not necessarily knows the machine learning algorithms to do coding. Neither machine language programming nor conventional programming is a substitute for each other. However, machine learning is a supplementation for conventional programming. For example, in an online trading platform project, machine learning in software development can build predictive algorithms. Simultaneously, the UI part, data visualization, and other elements of the program can be performed using conventional programming languages like Java or Ruby.
If the same problem is supposed to be solved using machine learning programming, the data engineers follow totally different approaches. They hardly develop algorithms; instead, they collect an array of historical data to build a semi-automatic model for the solution. Using a satisfactory set of data, they load it into already tailored ML-algorithms. As a result, the outcome is a model that predicts a new result of receiving new data as input.
Also, there is a significant difference between machine learning and conventional programming based on the number of input parameters that the model can process. In machine learning, to get an accurate prediction, it is required to feed thousands of parameters. Besides, it must be done with high accuracy, because every bit can affect the final result. However, in conventional programming, a programmer cannot build an algorithm following the same patterns.
How the role of machine learning programming developer is different than a conventional programmer?
The role of the machine learning programming developer is more “technical.” In other words, machine learning engineer has more in common with normal software developer than data scientist. However, a machine learning programming developer needs to work more with data, experiment with different machine learning algorithms, creates a prototype to design the solutions.
He must possess –
– Strong programming skills in one or more languages especially in Python
– More focused on machine learning algorithms instead of working in data analysis.
– Familiarity and knowledge of different ready-made machine learning algorithm libraries like Python, NumPy / SciPy.
– Knowledge of creating distributed applications using Hadoop and more.
Now, let’s see what a conventional programming language developer performs as part of the task.
A conventional programmer develops a loosely structured code based on the existing system. They don’t need to build an entire system by themselves. So, if machine learning programming is a new wave of programming, conventional programming is only a small part. A conventional programmer need to understand all the cycles of the software development lifecycle (SDLC)
We can conclude that machine learning programming algorithms focus on solving real-world issues by automated tasks across industries. These may range from on-demand music service to data security services. But conventional programming involves more manual interventions.