Il libro inizia con una panoramica dei diversi tipi di algoritmi di apprendimento automatico, tra cui l'apprendimento supervisionato, l'apprendimento non supervisionato e l'apprendimento per rinforzo. Gli autori discutono poi le varie tecniche di elaborazione dei dati che sono essenziali per l'apprendimento automatico, come la pulizia dei dati, l'ingegnerizzazione delle caratteristiche e la selezione dei modelli.Nei capitoli successivi, il libro copre un'ampia gamma di argomenti di apprendimento automatico, tra cui:Regressione: Una tecnica per la previsione di valori target continui.Classificazione: Una tecnica per la previsione di valori target categoriali.Clustering: Una tecnica per raggruppare punti di dati simili.Riduzione della dimensionalit�: Una tecnica per ridurre il numero di caratteristiche in un set di dati.Valutazione del modello: Una tecnica per valutare le prestazioni di un modello di apprendimento automatico.Il libro include anche un capitolo sul deep learning, un sottocampo dell'apprendimento automatico che ha guadagnato popolarit� negli ultimi anni.

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