Steps to use a ML model

1: Formulate the task

• Is this a regression problem or a classification one?
• Can this be done with Supervised Learning or Unsupervised Learning?
• What is the shape of the input data? What should the output data look like?

2: Prepare your data

• Clean your data and manually inspect it for patterns
• Shuffle your data before using it for training
• Normalise data before using it for training
• Convert data into tensors

3: Define and compile the model

1. Define your model using tf.sequential or tf.model then add layers using tf.layers
2. Compile the models with an optimizer, loss function, and parameters like batch size and epochs

4: Train the model

• Monitor training to see whether the loss is going down

5: Make predictions and evaluate

• Choose an evaluation metric for your model that you can monitor while training
• Once it’s trained, try making some test predictions to get a sense of model accuracy