These are chat archives for FreeCodeCamp/DataScience
discussion on how we can use statistical methods to measure and improve the efficacy of http://freeCodeCamp.com
There are 2 types of learning in ML - supervised & unsupervised.
Supervised learning uses the training data to find the 'results'.
Is the ML "looking" into its training data and "comparing" it with the input it has received and give a "mathematically" possible output?
@jkielsgaard Humans take in input mostly in visual & audio formats. For instance, we grow up seeing the alphabets, A-Z and hearing them from our teachers. They train us on how they come together to form words so that we can learn and pronounce words later - even those we weren't taught in class.
Applying the same logic to ML.
ML encompasses the whole process learning from data without being explicitly programmed. The process of showing the data to the algorithm is called training - training the machine to visualize and recognize the data. So that it can also learn with from it and eventually be able to predict/suggest (pronounce) future results (words) without being explicitly taught. In short, computers learn to do things without being repeatedly programmed (instructed).