7 Preparations You Should Make Before Using Machine Learning

Are you planning to take a machine learning course? If yes, then you are the right place. Machine learning is an excellent skill to have, especially in a time where most of the world is depending on technology to get the majority of the work done. Before you jump on to the bandwagon and start your course, there are a few preparations which you should make in order to smooth sail your way through the course.

Model building is one of the most important aspects of a machine learning course. There are a lot of algorithms and data which needs to be understood to be able to create an accurate model, which performs the job perfectly. So, what we basically mean is, machine learning includes lots and lots of data, you will have to manage it and not be intimidated by it.

What is machine learning in India?

The prospects of machine learning are excellent in this country. It is an all new world of data science, and you will most certainly have to have an understanding of data. You must also have knowledge of data tools like Python, in order to excel in this field. Plus, you will have to self-learn, before stepping into a course, so go through books, online study materials and videos, in order to prepare yourself for what is coming. If you want to know what is machine learning in India and how can it help you, then you ought to join a course. Go through online studies, and theories, practice them before setting your foot into the world of machine learning.

1.    Getting together all the data

As mentioned before, data is king when it comes to the deep learning of this subject. This happens to be a crucial step because the quantity, as well as the quality of the data, will determine the accuracy of your model. So be thorough with your data and compile it with a lot of care and attention.

2.    Prepare the data

There is nothing a little bit of prep cannot solve. Data preparation includes loading the data carefully, segregating it and then using it in machine learning. Keep an eye on things like the relationship between different variables, (if any), or look out for data imbalances as well. The data has to be divided into two parts, the first one will be used to train the model and the second will be required to evaluate the accuracy of the trained model. Chances are, that you might have to manipulate the data or adjust it, so keep going through it and try to make it as error-free as you can.

3.    Choosing the right model

It is one of the most crucial jobs. You need to have a proper workflow model! Just go through the different research models created by other data scientists, which are similar in nature and get a good model to make your work cohesive and accurate.

4.    Training

This is, hand’s down one of the most important steps in the course of the preparation. There are many features, in the training process, you have to initialize the experiments and attempt to predict the outcome. In the first instance, the model will definitely perform inaccurately. So you will have to train your model and keep on adjusting your values to have better and correct predictions. There is a lot of trial and error which goes into it. Go on repeating the process, and with each step you will notice the progress. With training the output will become more and more accurate.

5. Evaluate, evaluate, evaluate

The second set of data, which is saved for the evaluation stage comes into play now. This way, you will be able to test your model with new data, and this metric helps you to perfectly determine how accurately the model can perform. This will give you a good idea of how it would perform in real life situation and how much tweaking does it need to become perfect.

6.    Tune the parameters

The evaluation step is a tough one, so once you get past that, you will be charged up to improve your model and make it perfect. Parameter tuning is imperative, so go back the assumptions you made in the previous steps and try other values. Go through the training data set multiple times to get a more accurate result. The “hyperparameters” can be easily adjusted and tuned. By all means, all the tweaking is very experimental in nature and depends on your model, training process and data set.

7.    Prediction

As you must have realized this is one of the final steps in this series. Now you will be able to know, whether the model you have built with so much effort is being able to provide with accurate results or not. You can rely on your model to derive an inference with regards to the reason why it has been designed.

A deep learning of machine learning will require you to understand data and use it in the best way possible to derive the results you want from your model. There are several steps which will follow, but the aforementioned steps will help you build a strong foundation and delve deeper into a machine learning course.