Things you should know before entering the machine learning world

Things you should know before entering the machine learning world

Things you should know before entering the machine learning world
Things you should know before entering the machine learning world

There are many challenges that arise when implementing machine learning models in the real world, most of which are related to the incompatibility between the life cycle of machine learning and traditional software applications, where traditional software applications follow a relatively sequential model from design to production,As for machine learning models, they follow a circular life cycle that includes many aspects of the settlement or improvement that are unmatched in the current set of tools for traditional software applications.

Each stage of the life of "machine learning solutions" presents unique sets of challenges that do not exist in the world of traditional software, some of these challenges can be faced in different forms.The good news is that most of these challenges are solvable with the current machine learning frameworks and tools, however some solutions are far from the ultimate goal, so let's take a look at some of the challenges that you would like to know before you start learning the machine:

Introducing models to the final stage is much more than simple

When making a model, studying samples, and entering into a world of differential equations, one thing we do not realize until later is how difficult it is to deal with problems such as the decay of the model, the evaluation of models, the extent of success of each of them, development processes ... Etc.However, when ScienceOps was used, it was confirmed how wonderful it is to solve problems and reduce effort and time.

It is really difficult to learn to choose and extract traits

Something that you cannot learn within books but you will try to do is how to choose and extract features, as these skills are only learned through Kaggle contests and real-world projects,And learning about the different methods and methods for that is something that one learns only by implementing them or using them in real projects, and this takes a lot of time.

The evaluation stage is very important

Unless you apply the models to test the data, you have not performed predictive analyzes. Valuation techniques such as mutual verification, evaluation metrics, etc. are invaluable, which is simply dividing your data into test data and training data, as you will have a lot of creativity as you were able to define these two groups.

Also, we often do not discuss evaluation of models when explaining the work in front of one of them, that is, we do not talk about the techniques used to solve the problem, as saying “We used the SVM algorithm” does not tell us everything,It does not tell us the sources of your data and how to choose your features and evaluation methods.

Not easy

Learning Machine will spend a lot of time searching for the necessary resources. It is not enough just to run the model and choose the appropriate algorithm. You have to understand mathematics and take a lot of time to read and understand things and a lot of mental effort,If you see a resource that says "Learning Machine without Mathematics", get away from it immediately.

Machine learning is a small part of data science

Data science as a whole is a very large science, and machine learning is a small part of it, it includes data pre-processing, trait engineering, modeling, model training, checking models and displaying results, so it is important to pre-know other parts and know all the facts about data science.

When performing machine learning, almost all the time will be spent preparing data, extracting data, specifying features, etc. ...

Perceptions are important

Running machine learning algorithms is quite fun and cool, but it wouldn't be of much use if it couldn't be presented to other people without a machine learning background,As it is possible that the CEO and product manager of the company will not care about your code written in the R language, so visualizations, taking into account both technical and non-technical audiences, are very important in machine learning.

There are libraries for everything, all you need to do is get them

Usually most algorithms are implemented using the language R and PYTHON and this is a very important point, as it helps a lot in understanding the options for configuring each algorithm and knowing how it affects your model.

Most articles are valuable in books or written on papers

Most of the resources on the Internet among them “this article” will not give you the full concept of Machine Learning. They usually only make you follow what they say or do, but they do not give all the necessary skills or more of the examples provided so you end up reading books to understand.

Following the path of machine education is a major commitment in terms of effort and time so be prepared for that.

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