I'm studying with this book How to make AI / machine learning / deep learning apps with Python
--Classification ... Classify given data Examine and classify data characteristics
--Regression ... Predict future values from past performance Learn past data to make future numerical predictions
--Clustering ... Classify data into a set of similar things What is different from the classification is not to divide into predetermined items, but to classify similar items.
--Recommendation ... Derivation of relevant information on the data We recommend online shopping that suits your tastes
--Dimensionality reduction ... Reducing data features Identify characteristic data from large dimensions, reduce dimensions, and analyze data efficiently
--Image analysis Judging the object of the image --Voice analysis: Convert voice to text and determine what kind of sound it is --Text analysis: Sentence categorization, extraction of specific expressions, parsing
--Supervised Learning --The correct answer is given along with the data --Make predictions for unknown data --Unsupervised Learning --No correct answer is given --Find regularity from unknown data --Reinforcement Learning --The action gives a partial correct answer --Find the right buy from the data
A versatile data format useful for machine learning
--Comma separated data CSV format --ini file format
In addition, Numpy save format, pickle that can write Python objects as they are, etc.
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