Il corso Machine Learning e Data Mining in R è rivolto a chiunque voglia avere una pratica panoramica delle tecniche di apprendimento automatico, dalle più interpretabili - come l’analisi di regressione, delle componenti principali e dei gruppi - a quelle più flessibili come le reti neurali artificiali, sia shallow che deep - e le più ricorrenti problematiche di analisi e modellazione di dati e problemi reali - come collinearità, overfitting, regolarizzazione e knowledge transfer.
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Machine Learning e Data Mining in R
This course is part of Data Science con Python e R Specialization
Taught in Italian
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Instructors: Antonio Lepore
Included with
Course
Recommended experience
What you'll learn
Importare, manipolare e visualizzare dati mediante R e i pacchetti inclusi in tidyverse come dplyr e ggplot2
Riconoscere e risolvere in R, mediante i pacchetti aggiuntivi leaps, glmnet, pls, problemi di apprendimento supervisionato e non supervisionato
Comprendere le differenze tra reti neurali artificiali di tipo shallow e deep
Skills you'll gain
Details to know
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12 quizzes
Course
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There are 4 modules in this course
In questa week, ti introdurrò al linguaggio R: avrai una panoramica sulle strutture dati in R, su data wrangling e visualization. Imparerai ad usare i principali pacchetti R, tra cui i famosi dplyr e ggplot2, inclusi in tidyverse. Quando necessario, ti verranno fornite nozioni teoriche di base necessarie per una maggiore comprensione dei concetti implementati in R nei successivi moduli.
What's included
10 videos6 readings4 quizzes7 ungraded labs
In questa week, dopo aver introdotto la differenza tra metodi di apprendimento automatico (machine learning) supervisionato e non supervisionato, ti verranno illustrate le principali tecniche multivariate di esplorazione dei dati mediante R e i principali metodi di apprendimento automatico non supervisionato, come l'analisi dei gruppi (clustering) e l'analisi delle componenti principali (PCA).
What's included
6 videos1 reading4 quizzes9 ungraded labs
In questa week, approfondirai gli elementi di apprendimento automatico (machine learning) supervisionato. Imparerai ad applicare tecniche di predizione numerica a partire dai modelli lineari di regressione semplice e multipla. Ti sensibilizzerò verso i tipici problemi derivanti dall'applicazione della regressione lineare multipla a data set reali e le più comuni soluzioni attraverso la selezioni degli attributi e la regolarizzazione. Inoltre, ti verranno forniti strumenti pratici per la valutazione della capacità descrittiva (in-sample) e predittiva (out-of-sample) di un metodo di machine learning supervisionato e per la selezione del modello interpretativo migliore.
What's included
9 videos1 reading3 quizzes7 ungraded labs
In questa week ti introdurrò allo studio delle Reti Neurali Artificiali: partirai dal singolo percettrone, che è in grado di risolvere solo problemi di classificazione linearmente separabili, e, passando per il percettrone multilivello, che è in grado di risolvere problemi di classificazione e predizione numerica anche non linearmente separabili, arriverai alla "rivoluzione" del Deep Learning. Vedrai anche come è possibile utilizzare il Knowledge Transfer per addestrare le reti deep.
What's included
4 videos8 readings1 quiz
Offered by
Recommended if you're interested in Data Analysis
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