Machine Learning and Deep Learning- A perfect paradigm

The latest advancements in AI are so amazing that understanding them brings a virtual world in fronts of us like as we enter in the room and the environment of the room sets according to our mood; the lighting, the music, the temperature everything gets changed. A lot more imagination and questions come into our mind like what else can happen next, how will be the future and many more. But do you know AI revolves around the two terms i.e ML and DL? We are girdled by the advanced applications of AI subsets like Netflix, Facebook, Customer service etc. Many of us get confused and uses both the terms as interchangeable that is why it becomes more important to understand both of them.

Definition of Machine learning

A machine which can use the data, learn from it through algorithms and then implement them while making decisions. In short, to make them self-conscious.

For instance, if we see an example of on-demand music. When we listen to a new song or new artist during that time the station recommends the similar songs. How? It is just because of machine learning algorithms, the algorithm associates the song preferences with the preferences of other listeners.

Machine learning is based on complex coding related to mathematics, programming to drive a mechanical function such as a car, television etc. In simple words, whenever a machine starts understanding your language, like if you say “it’s dark” and the light gets turned “on”, it means the word “dark” recognised by the machine.

Definition of Deep learning

Deep learning is a subset of machine learning but they differ by their capabilities. Technically, ML may get wrong but the deep learning algorithm goes wrong, is slightly unbelievable.  Sometimes, the coding sends to machine learning become slightly typical and that time it needs guidance. But with deep learning, we do not require any guidance, it uses its own brain and work accordingly.

For instance, the light recognises only the word “dark” and it will not work if any other word is spoken. Like if we say “ Unable to see” or “light is not working” the ML program will not be able to catch the cue but on the other hand the DL algorithm will learn, compute and then figure out that light needs to be opened. DL uses its brain as we people do.

As ML and DL are becoming more refined, we will see more advanced applications then chatbot, driverless cars etc. The domain is vast and innovative and the biggest giants of the market are looking for Hadoop professionals so if you want to be the part there are multiple courses available over the internet such as Hadoop training, which can help you in enhancing knowledge and skills. In addition, you get the extra credentials to get more offers. Also, you will gain a strong technical background which in turn makes you more confident and peer in the market.

Recent inventions

Nowadays, machine learning is even combined with ArcGIS for better results in less time. Earlier, to obtain a correct resolution image from the satellite was a tough task, it took almost months to achieve the level and now can be done in a day. But, these solutions need multiple library and advanced platforms and it’s not necessary that they support ML and DL. So Esri Cloud is invented which provides a platform to avail a solution-centric approach. Here, AI is used considerably for language processing and computer vision. Also, it provides infrastructure support to the Machine Learning and Deep Learning.

One thought on “Machine Learning and Deep Learning- A perfect paradigm

  1. Awesome blog. I enjoyed reading your articles. This is truly a great read for me. I am looking forward to reading new articles. Keep up the good work!

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