STEM Specialization

hello, how are you friends programmers today prepare a blog to explain through STEM why you should learn machine learning so let’s get started.

let’s start by describing what STEM is
Getting a definition for STEM is easy enough: it stands for Science, Technology, Engineering, and Math
These are the kind of learning that are part of STEM, this being a program that is based on the fact that the participant must solve some problem and in this there will be no exact solution, instead the participant must find a way to solve the problem with the knowledge you already have. This requires a significant amount of creativity and flexible thinking, as well as technical knowledge and mastery of each individual discipline.

Now we go to the next part, because it would be important to take into account STEM in Machine Learning Developers, well this is simple and that machine learning consists of generating new ideas for the world of technology so this will open a wide space for things What is there to learn, especially math, so STEM is important because you will enter an environment where all your skills will be put to the test and your ideas will be released.

What makes this position interesting and unique?
Having the position of machine learning is something unique since all this consists of learning to generate your own ideas and bring them to life, it is a really difficult path but with effort you can go really far.

What makes this role similar to others?
what would make it equal to others would be the programming part since all these specializations and other issues have to do with programming. Another thing with which this role could be compared would be with the issue of full stack since at the end of the day in both you have to know a little of everything

What specific programming languages ​​and tools could one expect to work with in this position?

you would use tools such as: TensorFlow, Pandas, Keras, MongoDB, Numpy, among others although in the end

What is an example of a problem or a challenge someone in this role could solve or be asked to work on?
With this role you can get to do projects like Detection of fake news, Sports prediction, Prediction of diseases. Basically the range of the projects goes by the prediction and the data control.

What are some positives and negatives about this position?
The positive things as I have already mentioned is that ability to make your ideas come true, another advantage is that you can improve your learning over time and at your own pace since all your experience will prepare you for the future, in addition that experience allows you to identify patterns about problems already seen.

now on the side of the disadvantages we can see that being an experiential learning there may be times when you lack some kind of knowledge to be able to solve a problem, other disadvantages are that it can spend a lot of time and resources and since this is based on experience it is very easy to make mistakes.

Well that would be all we can see about machine learning and the reasons why you should dedicate yourself to this so with nothing more to add, see you later.