About
With the advent of more data and compute power, we have a unique opportunity to create more intricate algorithms that help address accuracy issues seen in traditional machine learning. Deep learning allows us to fine tune the fit of an algorithm to its data in an iterative manner, and to evaluate and “feed-back” that information during the training process for a better model. This results in better models if enough data is provided or in some cases if there’s an existing “base model”, as is the case in transfer learning, if good quality and highly representative data is provided.
This course aims to walk someone very new to ML (or even fairly experienced - see Level 3 material) through basic to advanced concepts with excerises and probing questions. The links are really nice, too.
This site was made in early 2018 and has been rebranded and reworked here.
Enjoy.
Does the github.com/rheartpython/navigating-ml deserve a star?
