Life Cycle of Machine Learning

Without being directly programmed, machine learning has allowed systems to learn on their own. But how exactly does a machine learning algorithm function?  It can be represented using the machine learning life cycle. The machine learning life cycle is a cyclical method for developing an effective machine learning project. The life cycle's primary goal is to find a solution to the issues or mission.

The most critical aspect of the whole procedure is understanding the main problem and knowing that it exists and what is the purpose of its existence.  So, before beginning the life cycle, we must first consider the problem since a good result is dependent on how better we understand the problem. To solve a problem in the full life cycle process, we create a machine learning algorithm called "model," and this model is generated by having "training." 

This cycle involves 7 steps.

The first phase in the machine learning life cycle is data collection. 

In this process, we must classify the various data sources, as data can be obtained from a variety of sources, including archives, databases, the internet, and mobile devices. It is one of the most crucial stages in the life cycle. The output's performance and efficiency will be determined by the quality and quantity of the data collected. The prediction will be more precise if there will be more data collected. 

This stage entails the following tasks: 

Determine different sources of data 

Gather information 

Combine the information which has been gathered from various sources.  

After the completion of the preceding process, we obtain a coherent collection of data, also known as a dataset. This set of data can be used in subsequent phases.

After the collection of data, we must prepare the data for further processing. Data planning is the process of putting our data in an appropriate location and preparing it so that we can use it in training machine learning. This step first involves grouping all of the data and then randomly select the position of the data. 

This stage is further subdivided into two procedures:

1. Exploration of Data 

It is used to comprehend the quality of the data with which we would be working. We must comprehend data features, format, and accuracy. 

and a deeper interpretation of the data results in a more successful outcome. We also see correlations, patterns, and outliers in this.

2. Pre-Processing of Data

Pre-processing of data is the next step; it is done for the analysis of data. 

The method of cleaning and transforming raw data into a usable format is known as data wrangling. It is the method of cleaning the data, choosing what variable to use, and for the purpose of analysis, converting the data into a suitable format for the next stage. It is one of the most crucial moves in the whole procedure. To fix the issue of quality, data must be cleaned.

The data collected may have some issues. For example:

Noise, Duplicate Data, Invalid Data, and Missing Values. So, to clean the data we use these filtering techniques. 

Now the data that has been cleaned and prepared will be analyzed in this step. Following things are involved in this step

The purpose of this phase is to create a machine learning model that will analyze the data with different computational methods and report on the results. It begins with determining the type of query, after which we pick machine learning methods like regression, classification, cluster analysis, association, and so on, and then we construct the model using prepared data and test it.

Now we train our model to increase its efficiency in order to achieve a better solution. 

 Datasets are used to train the model with different machine learning algorithms. It is necessary to train a model in order for it to learn the different patterns, laws, and functions.

We validate our machine learning model after it has been trained on a given dataset. In this step, we test our model's accuracy by feeding it a test dataset. 

The percentage accuracy of the model is determined by testing it against the project or issue requirements.

The final stage of the machine learning life cycle is implementation, in which we place the concept in a real-world environment. If the above-prepared model produces an effective outcome in accordance with our requirements at a reasonable speed, the model is deployed in the actual system. However, before deploying the project, we will examine whether or not it is optimizing its success using available data.

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