Summary

FAQs

• What is the difference between machine learning and deep learning?

  These are simply two different ways to build a model to make some sort of predictions for you. Machine Learning is a more traditional route where computers learn patterns within the data while Deep Learning is a more complex approach that tries to emulate the way the brain thinks in order to extract meaningful information from the data and make predictions.

• Do you build deep learning models, or just conventional machine learning models?

  I can build both machine learning and deep learning models. It will typically depend on the use case and jumping on a call to determine which approach is better a fit for your specific situation.

• How long does it take to build the model?

  This time it takes depends hugely on the situation. For example, a project working with a huge proprietary dataset may require weeks of cleaning, preprocessing, training, and evaluation while with a more simpler project can have all of those phases done within days.

• Do I have to provide a dataset, or can you build one yourself?

  For more niche and specific projects, it is better to provide your own dataset. However, in almost any case, a dataset can be harvested and built for a client. Datasets provided by clients will typically be better though, in most cases since they know their problem the best.