We all know that machine learning allows computer systems to work and learn automatically without human intervention. But still, one question arises: how does machine learning work? So, to answer the same I am going to make you aware of the working of machine learning through this article. Basically, there is a life cycle of machine learning and the focus of the process is to build an efficient project. The main goal of the life cycle of machine learning is to find a solution to the problem. There are various major steps in the life cycle of machine learning and they are-
- Collection of data
- Data preparation
- Data wrangling
- Analyse data
- Train the model
- Test the model
- Deployment
What is machine learning?
It is a subset of artificial intelligence which is mainly concerned with the study that gives computers the capabilities to learn from past data without human intervention. Machine learning is one of the most exciting and widely used technologies that one would have ever come across. It is an essential skill for analysts, data scientists and for those who transform a colossal amount of raw data into trends and predictions. It also opens various career paths for youngsters and professionals.
In the life cycle of machine learning, it is essential to detect a problem and to know the purpose of the problem because a good result depends on a better understanding of the problem. Actually what happens in order to get a good result or a solution to a problem we create a model. Now, creating a model requires training but to train the model, data is key therefore the cycle starts with the collection of data.
1- Collection of data
This is the first step and the main goal of this step is to detect and obtain all data-related problems. We collect data from various data sources such as files, databases, the internet, or mobile devices. This step is considered as the most important step in the life cycle because the quantity and quality of data decide the efficiency of the output or result. The more will be the data, the more accuracy will be in the prediction. There are also some tasks which come under this step.
- Filtering various data sources
- Gather data
- Now integrate the data obtained from various sources
2- Data Preparation
Once we are done with the collection of data, we need to prepare it for further steps. This is the step where we put this data in a suitable place to prepare it for the training of machine learning.
There is the two divisions or processes of this step which can be classified
Data exploration– in this we have to understand the nature of the data on which we have to work with. We need to understand various factors such as characteristics, format, and quality of data. This is essential because a better understanding of data leads to an efficient and effective outcome and in this we find correlations, general trends, and outliers.
Data reprocessing– in this we process the data for analysis.
3- Data Wrangling
It is the process of cleaning the data and transforming raw data into a usable format. Selecting the variable to use and then transforming the data into a proper format to make it more suitable for analysis. Actually, cleaning or data is necessary to address the quality issues. Sometimes it is not necessary that the data we have collected is always of our use as some of them may not be. There are multiple of issues in the collected data in real-world applications including-
- Missing values
- Duplicate data
- Noise
- Invalid data
It is essential to detect and solve the issue because it can negatively affect the quality of the outcome.
4- Data analysis
Now this step includes the following steps
- Selection of analytical techniques
- Building models
- Review the result
The main goal of this step is to build a model for machine learning to analyse the data using different techniques and review the outcome. There are various machine learning techniques such as Classification, Regression, Cluster analysis, Association etc. in this step we start with the determination of the type of problem and then we decide which techniques will be used to overcome the issue or to resolve it, then we build the model using prepared data, and evaluate the model. Further, we take the data and use machine learning algorithms to build the data.
5- Train the model
Now, we are ready to train the model with the help of our datasets. We are now able to train our model to improve its performance for better outcomes of the problem. In this process, we use machine learning algorithms. It is a prerequisite to train a model because it can understand the various patterns, rules and features.
6- Test Model
Now, once we train our model on a given dataset, then we test the model which we have trained. In this step, we basically check the accuracy of the model by providing a test dataset to it. Model testing determines the percentage of accuracy in the model as per the requirement of the project or problem.
7- Deployment
This is the final step in the life cycle of machine learning where we apply the model to real-world systems. We can deploy the model in the real system if it is providing accuracy according to our requirements and with an acceptable speed. It is also necessary to check
Conclusion
The above article is all about the life cycle of machine learning training and these are essential to achieve the required goals. Above mentioned steps is an in-depth picture of the stages of machine learning. In order to meet the required solution of a problem or project is it necessary to know the steps which you have to take in the development process. It is used widely across the globe in different industries in order to make machinery automatic so that the productivity of an organization can be enhanced. Apart from this, it also opens a wide range of career opportunities for many who want to explore themselves in this field. One who wants to make a career in this needs proper machine learning training and for that, you can visit the APTRON institute in Noida.
