What are the key steps in a machine learning project workflow?

What are the key steps in a machine learning project workflow?

The key steps typically include problem definition, data collection, data preprocessing, model training, evaluation, and deployment.

Can you explain the data preprocessing step?

Sure! Data preprocessing involves cleaning the data, handling missing values, normalizing or scaling features, and encoding categorical variables.

What tools can I use for model training?

Popular tools for model training include TensorFlow, PyTorch, Scikit-learn, and Keras, depending on your specific needs.

How do I evaluate the performance of my model?

You can evaluate your model using metrics like accuracy, precision, recall, F1 score, and ROC-AUC, depending on the problem type.

What should I consider for deployment?

For deployment, consider scalability, monitoring, versioning, and the environment where the model will run.