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DIDATTICO","\u003Ch1 class=\"fw-semibold text-uppercase\">Alberi decisionali e \u003Cspan class=\"text-color-primary-400\">metodi di ensemble\u003C/span>\u003C/h1>\n","\u003Cp class=\"font-size-body-1 text-color-grey-800\">Approfondimento su alberi decisionali e metodi di ensemble, includendo teoria, applicazioni pratiche e tecniche avanzate.\u003C/p>\n","\u003Ch2>Descrizione\u003C/h2>\n\u003Cp class=\"font-size-body-1\">La trattazione copre \u003Cstrong>alberi decisionali\u003C/strong> e \u003Cstrong>metodi di ensemble\u003C/strong>, iniziando con concetti base come \u003Cstrong>Gini impurity\u003C/strong>, entropia e information gain. Include applicazioni pratiche su dataset Iris e Bill Authentication.\u003C/p>\n\u003Cp class=\"font-size-body-1\">Approfondisce \u003Cstrong>ensemble learning\u003C/strong>, \u003Cstrong>random forest\u003C/strong>, \u003Cstrong>boosting\u003C/strong>, \u003Cstrong>Adaboost\u003C/strong>, \u003Cstrong>gradient boosting\u003C/strong> e \u003Cstrong>XGBoost\u003C/strong>, con esercitazioni dettagliate. Esamina anche Catboost e la sua applicazione al dataset Titanic, offrendo un’esperienza completa e bilanciata tra teoria e pratica.\u003C/p>\n",{"title":789,"list":790,"link":783},"DOMANDE FREQUENTI",[791,794,797,800,803,806],{"title":792,"text":793},"Quali competenze sono necessarie per seguire il modulo Alberi decisionali e metodi di ensemble?","\u003Cp class=\"fw-regular font-size-body-1\">\u003Cspan class=\"text-color-grey-800\" data-sheets-root=\"1\" data-sheets-value=\"{"1":2,"2":"Per seguire il modulo Alberi decisionali e metodi di ensemble sono necessarie competenze preliminari specifiche di Python, Pandas, Numpy, Scikit-learn, Data Visualization."}\" data-sheets-userformat=\"{"2":4482,"4":{"1":2,"2":16750848},"10":2,"11":4,"15":"Arial"}\">Per seguire il modulo Alberi decisionali e metodi di ensemble sono necessarie competenze preliminari specifiche di Python, Pandas, Numpy, Scikit-learn, Data Visualization.\u003C/span>\u003C/p>\n",{"title":795,"text":796},"Quali sono i principali argomenti trattati nel modulo Alberi decisionali e metodi di ensemble?","\u003Cp class=\"fw-regular font-size-body-1\">\u003Cspan class=\"text-color-grey-800\" data-sheets-root=\"1\" data-sheets-value=\"{"1":2,"2":"Il modulo Alberi decisionali\\ne metodi di ensemble copre i seguenti argomenti principali: alberi decisionali, Random forest, Boosting, Adaboost, Gradient Boosting, Catboost."}\" data-sheets-userformat=\"{"2":4482,"4":{"1":2,"2":16750848},"10":2,"11":4,"15":"Arial"}\">Il modulo Alberi decisionali e metodi di ensemble copre i seguenti argomenti principali: alberi decisionali, Random forest, Boosting, Adaboost, Gradient Boosting, Catboost.\u003C/span>\u003C/p>\n",{"title":798,"text":799},"Quali nozioni principali posso apprendere al termine del modulo Alberi decisionali e metodi di ensemble?","\u003Cp class=\"fw-regular font-size-body-1\">\u003Cspan class=\"text-color-grey-800\" data-sheets-root=\"1\" data-sheets-value=\"{"1":2,"2":"Al termine del modulo Alberi decisionali e metodi di ensemble sarai in grado di comprendere e applicare alberi decisionali e metodi di ensemble, utilizzando tecniche come Gini impurity, entropia e information gain, e applicando ensemble learning, random forest, boosting, Adaboost, gradient boosting e XGBoost su dataset come Iris e Bill Authentication."}\" data-sheets-userformat=\"{"2":4482,"4":{"1":2,"2":16750848},"10":2,"11":0,"15":"Arial"}\">Al termine del modulo Alberi decisionali e metodi di ensemble sarai in grado di comprendere e applicare alberi decisionali e metodi di ensemble, utilizzando tecniche come Gini impurity, entropia e information gain, e applicando ensemble learning, random forest, boosting, Adaboost, gradient boosting e XGBoost su dataset come Iris e Bill Authentication.\u003C/span>\u003C/p>\n",{"title":801,"text":802},"Al termine del modulo sono previsti un esame finale e una certificazione?","\u003Cp class=\"fw-regular font-size-body-1\">\u003Cspan class=\"text-color-grey-800\" data-sheets-root=\"1\" data-sheets-value=\"{"1":2,"2":"L'esame finale e la certificazione sono previsti per i corsi Data Masters, non per i singoli moduli."}\" data-sheets-userformat=\"{"2":4995,"3":{"1":0},"4":{"1":2,"2":16750848},"10":2,"11":4,"12":0,"15":"Arial"}\">L’esame finale e la certificazione sono previsti per i corsi Data Masters, non per i singoli moduli.\u003C/span>\u003C/p>\n",{"title":804,"text":805},"Il materiale del modulo è scaricabile? Posso rivedere le lezioni?","\u003Cp class=\"fw-regular font-size-body-1\">\u003Cspan class=\"text-color-grey-800\">Tutti i materiali (slide, materiale per esercitazioni, ecc.) sono scaricabili, mentre tutte le lezioni possono essere riviste in qualsiasi momento.\u003C/span>\u003C/p>\n",{"title":807,"text":808},"In caso di dubbi e domande, è disponibile assistenza da parte dei docenti?","\u003Cp class=\"fw-regular font-size-body-1\">\u003Cspan class=\"text-color-grey-800\" data-sheets-root=\"1\" data-sheets-value=\"{"1":2,"2":"Certamente, durante il modulo Alberi decisionali\\ne metodi di ensemble è disponibile assistenza continua da parte dei nostri docenti per qualsiasi dubbio o domanda."}\" data-sheets-userformat=\"{"2":4482,"4":{"1":2,"2":16750848},"10":2,"11":4,"15":"Arial"}\">Certamente, durante il modulo Alberi decisionali e metodi di ensemble è disponibile assistenza continua da parte dei nostri docenti per qualsiasi dubbio o domanda.\u003C/span>\u003C/p>\n",[810,813,817,820],{"icon":811,"caption":71,"text":812},"icon-level","Da Beginner ad Advanced",{"icon":814,"caption":815,"text":816},"icon-round-check","PREREQUISITI","Python per la ds, Pandas, Numpy, Scikit-learn, Data visualization",{"icon":818,"caption":70,"text":819},"icon-calendar","6 ore",{"icon":821,"caption":822,"text":823},"icon-lessons","LEZIONI","100% on demand",[825],{"title":826,"items":827},"Alberi Decisionali e metodi di Ensemble",[828,831,833,835,837,839,841,843,845,847,849,851,853,855,857,859],{"title":829,"text":830},"Alberi Decisionali","\u003Cp>\u003Cspan class=\"text-color-grey-700\">Nessun dettaglio\u003C/span>\u003C/p>\n",{"title":832,"text":830},"Gini impurity",{"title":834,"text":830},"Entropy and Information Gain in Decision Trees",{"title":836,"text":830},"Alberi decisionali su dataset Iris",{"title":838,"text":830},"Partizionamento spazio decisionale con alberi",{"title":840,"text":830},"Ensemble learning e random forest",{"title":842,"text":830},"Random Forest su dataset Bill Authentication",{"title":844,"text":830},"Boosting",{"title":846,"text":830},"Adaboost",{"title":848,"text":830},"Gradient Boosting",{"title":850,"text":830},"Esercitazione Gradient Boosting",{"title":852,"text":830},"Cenni su XGBoost",{"title":854,"text":830},"Esercitazione con XGBoost",{"title":856,"text":830},"Cenni su Catboost",{"title":858,"text":830},"Catboost su dataset Titanic",{"title":860,"text":830},"Esercitazione su algoritmi di ensemble",{"list":862,"intro":1022},[863,915,1005],{"id":335,"date":864,"title":332,"description":865,"url":333,"image":866,"duration":874,"prerequisite":875,"regular_price":876,"taxonomies":877,"level":271,"type":169},"2024-06-04T16:10:06+02:00","Algoritmi di Machine Learning è il corso per apprendere tutti gli algoritmi di Machine Learning con Python. 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