In machine learning the goal of training is to create an accurate model that answers questions correctly, most of the time.
In order to train a model, we need correct data.
The quality and quantity of the data acquired will determine the quality of results.
We need to prepare the data, which includes randomizing. Also some of the data is earmarked for training and other data is used for evaluation.
Choosing a machine learning model is an important step and is pertinent to what is being evaluated.
The training phase is the bulk of the machine learning workflow.
The model usually does a poor job in the beginning, but we can then help it make better decisions by adjusting it over iterative phases to reach better conclusions.
At some point one needs to determine when the adjustments are adequate for one’s needs, since the adjustments can go on forever.
Machine learning is used for answering questions and making predictions using models and data rather than human judgement.
Tensorflow is an open source machine learning library for research and production and is a great place to experiment and learn.