Today we are going to learn about the steps required for the machine learning process to build a machine learning project. if you are going to build a project in machine learning then you must follow these required steps to successfully complete a machine learning project.
Steps for the Machine learning process
- Data collection
- Data processing and preparation
- Feature engineering
- Model selection
- Model training and data pipeline
- Model validation
- Model persistence
What is Data Collection?
Data collection means collecting the raw data sets for our machine learning project. this is the first step we need to follow to process a machine learning project. we need to collect some data sets for training our machine learning model.
we can collect this data from anywhere. like from open source projects, from the web, internet, or places, etc, and this collection of the data set is used for training our machine learning model. and make sure the data we collect for our model is relevant because if our collected data is irrelevant then our model should not be trained in a way we want and our model will produce the wrong output.
What is Data Processing and Preparation?
When we collect relevant data for our model then we need to process and prepare this collected data. because the data we collected is raw data. in this collected data may not be in a usable format. there are some missing data that we need to handle before training our model.
Note – after processing and preparation, our data will be in a usable format.
What is Feature Engineering?
After processing our data we need to apply feature engineering to our data. in feature engineering, we transform our data. like as per our need, we need to delete some rows of our data or add some data. so it will optimize well and train well our model using the data set.
What is the Model selection?
After performing feature engineering we need to select a model based on our data set. and it is the main task in our processing step of machine learning. we don’t need to make a new model. there is much pre-trained available data from which we can select our particular model.
What is the Model training and data pipeline?
After the selection of the model, we need to make a pipeline that we can use to train our model. and our data pipeline should be in an inefficient form. because our training model should take a long time.
What is Model validation?
After training our model we need to validate our model. and for validating our model we need to take the different data set that a model cannot see in the past. but the data set that we include training our model should come from the same distribution from which our training data set comes.
What is Model Persistence?
After performing all the things and validating our model we need to persist in our model. means our model should be available for new users. so our model should be properly saved and then push the model in production.
Conclusion of the post
In brief, I can say then take a raw data set and process it for a given task. and deal with missing data sets and perform normalization and feature engineering on the data set. and then select a relevant model. and then add code in the model and then train the model using our data set. and then validate the model and process the thing that can improve our model performance.
Note – so these are the steps that we need to follow to process a machine-learning model.
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