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DIDATTICO","\u003Ch1 class=\"fw-semibold text-uppercase\">Introduzione al \u003Cspan class=\"text-color-primary-400\">ML\u003C/span>\u003C/h1>\n","\u003Cp class=\"font-size-body-1 text-color-grey-800\">Introduzione al mondo del Machine Learning, dalla teoria alla pratica, coprendo implementazioni e tecniche di regressione.\u003C/p>\n","\u003Ch2>Descrizione\u003C/h2>\n\u003Cp class=\"font-size-body-1\">Il modulo “\u003Cstrong>Introduzione al ML\u003C/strong>” offre una panoramica dettagliata del Machine Learning, partendo da una presentazione del corso e dei concetti fondamentali. Esamina il processo di implementazione, il \u003Cstrong>No Free Lunch Theorem\u003C/strong>, e approfondisce le tecniche di regressione lineare con \u003Cstrong>SGDRegressor\u003C/strong> e \u003Cstrong>LinearRegression\u003C/strong>.\u003C/p>\n\u003Cp class=\"font-size-body-1\">Vengono inoltre trattati la regressione logistica e l’uso di \u003Cstrong>LogisticRegression\u003C/strong> e\u003Cstrong> make_pipeline\u003C/strong>, fornendo una solida base teorica 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 Introduzione al ML?","\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 Introduzione al ML 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 Introduzione al ML 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 Introduzione al ML?","\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 Introduzione al ML copre i seguenti argomenti principali: introduzione al Machine Learning, processo di implementazione, No Free Lunch Theorem, regressione lineare e logistica."}\" data-sheets-userformat=\"{"2":4482,"4":{"1":2,"2":16750848},"10":2,"11":4,"15":"Arial"}\">Il modulo Introduzione al ML copre i seguenti argomenti principali: introduzione al Machine Learning, processo di implementazione, No Free Lunch Theorem, regressione lineare e logistica.\u003C/span>\u003C/p>\n",{"title":798,"text":799},"Quali nozioni principali posso apprendere al termine del modulo Introduzione al ML?","\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 Introduzione al ML sarai in grado di comprendere i concetti fondamentali del Machine Learning, conoscere il processo di implementazione e applicare il No Free Lunch Theorem. Sarai capace di utilizzare tecniche di regressione lineare con SGDRegressor e LinearRegression, e di applicare la regressione logistica utilizzando LogisticRegression e make_pipeline, ottenendo una solida base teorica e pratica."}\" data-sheets-userformat=\"{"2":4482,"4":{"1":2,"2":16750848},"10":2,"11":4,"15":"Arial"}\">Al termine del modulo Introduzione al ML sarai in grado di comprendere i concetti fondamentali del Machine Learning, conoscere il processo di implementazione e applicare il No Free Lunch Theorem. Sarai capace di utilizzare tecniche di regressione lineare con SGDRegressor e LinearRegression, e di applicare la regressione logistica utilizzando LogisticRegression e make_pipeline, ottenendo una solida base teorica e pratica.\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 Introduzione al ML 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 Introduzione al ML 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","2 ore",{"icon":821,"caption":822,"text":823},"icon-lessons","LEZIONI","100% on demand",[825],{"title":826,"items":827},"Introduzione al Machine Learning",[828,831,832,834,836,838,840,842],{"title":829,"text":830},"Introduzione al Corso","\u003Cp>\u003Cspan class=\"text-color-grey-700\">Nessun dettaglio\u003C/span>\u003C/p>\n",{"title":826,"text":830},{"title":833,"text":830},"Processo di Implementazione",{"title":835,"text":830},"No Free Lunch Theorem",{"title":837,"text":830},"Regressione Lineare",{"title":839,"text":830},"SGDRegressor e LinearRegression",{"title":841,"text":830},"Regressione Logistica",{"title":843,"text":830},"LogisticRegression e make_pipeline",{"list":845,"intro":1005},[846,898,988],{"id":335,"date":847,"title":332,"description":848,"url":333,"image":849,"duration":857,"prerequisite":858,"regular_price":859,"taxonomies":860,"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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