Training Set
- Figure out what you want from the data.
- Determine the type of machine learning model you need, standard ML algorithms or artificial neural network.
a. If a standard ML algorithm, then it could use K nearest neighbor or Naive Bayes.
b. If a neural network, feed a training set and determine how much to change the weights.
c. Use backpropagation to adjust the weights to lower the cost function, i.e., the system will go back through the network and adjust the dials to increase its accuracy.
Test Set
- Data will be added from the test set. The test set will not be labeled.
- If the data in the training set is not sufficient for the test set to do its classifications, then that’s called “overfitting” the data.AI systems are only as good as the data they are fed. They can learn by trying different things. They can do things humans cannot–and vice versa. AIs can process large amounts of data quickly and see patterns we cannot, but they still need supervision and direction.