One misconception I would like to dispel early on is that implementing the ML algorithm itself is the bulk of the work you'll need to do to accomplish some task. If you're new to this, you may be under the impression that 95% of your time should be spent on implementing a neural network, and that the neural network is solely responsible for the results you get. Build a neural network, put data in, magically get results out. What could be easier?
The reality of ML is that the algorithm you use is only as good as the data you put into it. Furthermore, the results you get are only as good as your ability to process and interpret them. The age-old computer science acronym GIGO fits well here: Garbage In, Garbage Out.
When implementing ML techniques, you must also pay close attention to their preprocessing and postprocessing of data. Data preprocessing is required...