ML steps Define the problem Data Collection Data preprocessing Choose a model split the data into training & testing(Following 80-20 rule) Evaluate the model Hyperparameter tuning Cross validation Model finalization Model deployment

cross validation is dividing the data into different sub data to fid out model accuracy

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

DONE

done once again i love you ammar

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

done once again

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

Done

Done