Traditional coding vs Machine Learning – Simple explanation for non-technical people

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  • Is Machine learning an extension of traditional coding?
  • Does Machine learning involve more complex code?
  • Do we need to be proficient in technology to understand Machine Learning?

One year back, I had these questions when I used to hear about Machine Learning or Artificial Intelligence.

I used to think that AI is some kind of super technology which requires complex coding and can only be learned by hardcore tech people.

But then I started learning ML/AI just to be aware of the latest technology developments and I discovered the real truth.

The basic difference between Traditional coding and Machine Learning

In Traditional coding, you write a set of rules (expressed in a programming language), provide inputs and expect a certain output. 

Basically “you” (as a programmer) define everything and the computer just follows the set of rules and gives you the output.

But then a few smart people thought – why are we doing all the stuff? can the computer figure out the “set of rules” itself?

Our life would be simpler in that case.

So, in Machine learning, you provide the computer with the input as well as the output and ask it to figure out the set of rules. 

Essentially “you delegate” the stuff and let the more specialized one (computer with more processing power) do the work

In a way, a machine learns some kind of insight from the existing data and then performs a task on the new data.

Yep, that’s Machine Learning! That’s it. No big deal.

If you want to check, how easy or complex it is to learn AI models, then try going through this article – Building a basic ML model in just 4 minutes

Is Machine learning an extension of traditional coding?

No, we can’t say it like this. It’s a parallel field that focuses on training algorithms to learn patterns and relationships from data

However, coding is still necessary to implement machine learning algorithms and process the data. We use the same programming language like Python or Go.

ML/AI is model-based.

There is a model to do every task. For ex – There is a model to convert Text to Speech, there is a separate model to convert Speech to text, there is a separate model to predict house prices in Texas and so on.

Mostly we use Python to access these models, talk to these models and get the output from them.

One major difference is, ML requires more computational power.

So our normal CPUs might not suffice and therefore we need GPUs (Graphic Processing Units) which were earlier used for playing computer games or TPUs (Tenson Processing Units)

Read Can AI do everything? If not, what are the things AI can do today?

Do I have to build an AI model to use it?

In order to use an AI model, you don’t have to build it. You can use it if it’s open-sourced.

Do we need to be proficient in technology to understand Machine Learning?

No, that’s not required if you want to learn from a functional point of view.

If you want to build AI models then you need to learn Maths, Statistics, Data Science, Python, SQL, Logical thinking and some domain knowledge.

In case you want to develop some AI tools using those models then you need to learn Python and some other libraries and frameworks.

Build an AI video generation tool yourself using this Generative AI code and see how simple it is.

Thanks for choosing to read it completely. I can try to simplify any AI concept for you.

In case you want me to simplify any AI topic, please let me know on LinkedIn – https://www.linkedin.com/in/nikhileshtayal/

Happy learning!

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Nikhilesh Tayal
Nikhilesh Tayal
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