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Karlos Bajaj
Karlos Bajaj

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Machine Learning is Different from Traditional Programming

Traditional programming has been there now for more than a century. Traditional programming is any computer programming that is manually created and makes use of input data and produces the desired output. But it has been several decades that a particular type of programming has actually revolutionized business and this is particularly applicable in the field of intelligence as well as embedded analytics.

Machine learning is different from traditional programming and it will be seen in the sections that follow, how exactly the two programming ways are different.

How is machine learning different from traditional programming?

To begin with, the approaches to solve the different problems in case of traditional programming and machine learning are completely different. The ways to handle the different tasks are also different for machine learning and traditional programming.

An in-depth understanding

In the case of traditional programming, the programmer writes clear instructions and the computer needs to follow those instructions and generate the desired output. These instructions explicitly determine how the computer should process the input data which is given to produce the desired output. What is needed is an in-depth understanding of the problem involved and a clear and proper way to drive the process of creating the apt code to ensure that the right output is delivered.

Machine learning is different from traditional programming as in the case of machine learning, the programmer does not write explicit rules for the computer to follow. Instead, a model is trained using a very large dataset. The model is supposed to learn the different patterns and relationships from this huge dataset. This enables it to make predictions or in other words decisions and the need for explicit programming for each possibility does not arise. This approach to boost efficiency with AI tools is extremely useful in case of problems which are very complex in nature and particularly in cases where the possibility to write explicit instructions is very difficult or almost impossible.

The factor associated with data dependency

When it is about dealing with traditional programming, then the dependency on data is less and the quality of the output is heavily dependent on the type of logic that the programmer makes use of.

Machine learning is different from traditional programming and when dealing with machine learning, there is heavy reliance on data. The quality as well as quantity of the dataset being used have an immense impact over the performance as well as the accuracy of the model.

The matter with flexibility

In terms of flexibility, traditional programming offers limited flexibility. When changes are made in the problem domain, then manual updates are required to be done to the code.

When the model is retrained with the data that is updated then higher adaptability is offered especially when it is about dealing with new scenarios. This is how people boost efficiency with AI tools.

The complexity of the problem

Traditional programming is the perfect match for problems that are associated with clear logic that is also deterministic.

Machine learning is different from traditional programming and is preferable in cases where the problem is complex and the patterns and the relationships are not quite obvious and this could include image recognition, predictive analysis as well as natural language processing.

The development process

The process of development is generally linear as well as predictable in case of traditional programming and the main focus is on implementing and also debugging logic that is predefined.

Machine learning is different from traditional programming and is associated with a process that is iterative in nature and it involves training, evaluating and also fine tuning the models as per the requirements. The entire process is more experimental in nature instead of being predictive.

The predictability factor

In the case of traditional programming, the output is predictable and this is particularly true when the logic is known and so are the inputs.

In the case of machine learning, the predictions that are made by the model could prove to be less interpretable and this is particularly applicable in cases of models which are complex in nature, for instance, deep neural networks.

The comparisons that are made between traditional programming and machine learning help professionals choose the most appropriate approach to solve the problems and utilize computer systems in the best possible way. Traditional programming has been in use for quite some time. Now machine learning along with artificial intelligence is gaining quite a bit of traction and is becoming a favourite solution for businesses, developers as well as consumers.

Final Note

Machine learning is best suited in cases of tasks that are associated with complex algorithms, pattern recognition, statistical models and decision making. In short, it is used to boost efficiency with AI tools. Traditional programming has been a common approach for problem solving and for tasks that rely on language that is rule-based, precise scenarios of input and output, and where variation does not play an important role.

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Heroku

Build apps, not infrastructure.

Dealing with servers, hardware, and infrastructure can take up your valuable time. Discover the benefits of Heroku, the PaaS of choice for developers since 2007.

Visit Site

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