Let us learn the life cycle of machine learning.

Life Cycle of Machine Learning :

1] Data Gathering :

• Data gathering is the first step of machine learning life cycle.
• The goal of this step is to identify and obtain all data related problems.
• In this step we need to identify the different data sources, such as files, internet or mobile devices.
• It is one of the most important step of this life cycle.
• The more the data the more accurate will be the prediction.

2] Data Preparation :

• After collecting the data we need to prepare for father steps.
• Data preparation is step where we put our data to a suitable place for preparation to use it on machine learning training.
• In this step we must understand the nature of the data that we have to work with.
• And to understand the characteristics, format and quality of the data.

3] Data Wrangling :

• Data wrangling is the process of cleaning and converting raw data into a usable format.
• It is the process of cleaning the data and transforming the data into a proper format to make it more useable for the next in this step.
• In this step various filtering techniques are used to clean the data.

4] Data Analysis :

• The clean and prepared data is passed on to the analysis step.
• The aim of this step is to build a machine learning model by analysing the data using various techniques.
• Then the model is build using prepared data and model is evaluated.
• In this step we take the data and use machine learning algorithm to build the model.

5] Training Model :

• In this step we train the model to improve its performance for better outcome.
• There is use datasets to train the model using various machine learning algorithms.
• Training a model is required to understand various rules and features.

6] Testing Model :

• After machine learning model is trained we need to test the model.
• In this step the accuracy of the model is checked by giving test data.
• By testing the model we can determine the accuracy of the model as per the requirement.

7] Deployment :

• The last step of machine learning life cycle is deployment.
• If the prepared model is providing accurate outcome as per the requirements then we deploy the model in real world system.