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I learned about the steps of Building ML models from A-Z:
1: Define the problem
2: Collect the data
3: Data preprocessing (which takes 80% of the time)
4: Choose a Model/Models
5: Split the data into Training and testing data sets (usually 80% training and 20% testing)
6: Evaluate the Model
7: Hyperparameter Tuning
8: Cross Validation (Multi-dimensional training & testing)
9: Model finalization
10: Model Deployment
11: Retest, Update, Monitor the Model
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done once again
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Steps to consider While Building and Deploying a Machine Learning Model:---(1). Define Your Problem (2). Data Collection (3). Data Preprocessing (4). Choosing a Model (5). Splitting the Data (6). Evaluating the Model (7). Hyperparameter Tuning (8). Cross Validity (9). Finalizing the Model (10). Deploying the Model
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Done
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Done
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Machine learning Steps
Define the problem 2. data collection 3. data preprocessing 4.choose a model 5. splitting the data 6. evaluation the model 7. hyper parameters tuning 8. cross validation 9. model finalization 10. deploy the model 11. retest , update
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behtreen
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💗
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Good
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Learning 11 steps
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