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Machine Learning Projects follow the given cycle:
- Problem definition: Clearly define the problem you are trying to solve and how it can be addressed with machine learning.
- Data collection and preprocessing: Gather the necessary data and preprocess it to make it suitable for use in a machine learning model.
- Exploratory data analysis: Analyze the data to gain a deeper understanding of its characteristics and identify any potential issues.
- Model design: Choose an appropriate machine learning model for the problem and design it.
- Training: Train the model on the preprocessed data.
- Evaluation: Evaluate the performance of the model on a hold-out set of data.
- Deployment: Deploy the model in a production environment and monitor its performance.
- Maintenance: Continuously improve and update the model as new data becomes available.
Note that depending on the complexity of the problem and the size of the data set, some phases can be more or less detailed and time-consuming.
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Happy Learning and Keep Learning…📖📖