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Antonio Lepore, Biagio Palumbo, and Carlo Sansone

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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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.

La modalità di erogazione del corso è di tipo learning by doing, mediante una continua implementazione in R dei concetti esposti. Le diverse unità ti verranno prima illustrate a voce, per permetterti di ricordare e capire, e poi rese disponibili sotto forma di reading, per permetterti di analizzarne criticamente il contenuto. Alla fine di ogni unità, verrai messo alla prova attraverso open Lab in ambiente di sviluppo RStudio, che ti permetteranno di applicare i metodi trattati nel corso ai tanti data set reali che ti saranno forniti. Ti verrà infine richiesto di valutare i tuoi progressi mediante graded quiz contenenti domande a risposta multipla. Non rimandare: Machine Learning e Data Mining in R sono ora a portata di mano!

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What's inside

Syllabus

Elementi di R
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.
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Apprendimento automatico non supervisionato
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).
Apprendimento automatico supervisionato
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.
Reti Neurali e Deep Learning
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.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Esamina le tecniche utilizzate nell'industria, rendendo gli argomenti rilevanti per il mondo del lavoro
Utilizza R, lo strumento standard nel settore dei dati
Fornisce una panoramica pratica delle tecniche di apprendimento automatico, sia interpretabili che flessibili
Esplora problemi reali di analisi e modellazione dei dati, come la collinearità e l'overfitting
Adotta un approccio pratico, consentendo agli studenti di implementare i concetti in R
Include open lab in RStudio per applicare i metodi a dataset reali

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Reviews summary

Highly praised r learning course

This course on Machine Learning and Data Mining using the R programming language introduces statistical modeling and machine learning methods using R software. The hands-on approach with Lab exercises in RStudio will help you apply what you learn.
5-star review.
"O melhor que fiz no Coursera até agora"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Machine Learning e Data Mining in R with these activities:
Ripartire dalle basi di matematica e statistica
Migliora la comprensione del data mining e dell'apprendimento automatico rinfrescando le tue conoscenze di matematica e statistica.
Browse courses on Algebra
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  • Rivedi i concetti di base in matematica, come algebra e calcolo.
  • Rivedi i concetti di base di statistica, come probabilità e inferenza statistica.
  • Pratica le tue abilità attraverso esercizi e problemi.
Esercizi di data wrangling e visualizzazione in R
Rafforza le tue capacità di data wrangling e visualizzazione in R per migliorare l'efficienza e l'efficacia del tuo lavoro con i dati.
Browse courses on R
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  • Importa ed esplora set di dati reali utilizzando le funzioni di R per il data wrangling.
  • Visualizza i dati utilizzando diversi tipi di grafici, inclusi grafici a barre, grafici a torta e grafici di dispersione.
  • Pratica la pulizia e la preparazione dei dati per l'analisi.
Tutorial sull'applicazione degli algoritmi di apprendimento automatico non supervisionati in R
Approfondisci la tua comprensione dell'apprendimento automatico non supervisionato in R seguendo tutorial guidati e applicandoli a set di dati reali.
Browse courses on Clustering
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  • Esplora algoritmi di clustering come k-means e hierarchical clustering.
  • Applica tecniche di analisi delle componenti principali per ridurre la dimensionalità dei dati.
  • Analizza i risultati e interpreta i modelli identificati nei dati.
Two other activities
Expand to see all activities and additional details
Show all five activities
Tutoraggio studenti alle prime armi di apprendimento automatico
Consolida le tue conoscenze e sviluppa abilità comunicative mentorizzando gli studenti alle prime armi nell'apprendimento automatico.
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  • Offeriti come tutor presso il tuo istituto o tramite piattaforme online.
  • Prepara materiali e risorse per aiutare gli studenti a comprendere i concetti.
  • Rispondi alle domande, fornisci feedback e incoraggiamento.
Contribuisci al pacchetto R per l'apprendimento automatico
Approfondisci le tue conoscenze pratiche e teoriche contribuendo a un pacchetto R esistente per l'apprendimento automatico.
Browse courses on R
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  • Individua un pacchetto R per l'apprendimento automatico a cui vorresti contribuire.
  • Studia il codice sorgente e comprendi la struttura e la funzionalità del pacchetto.
  • Identifica un'area di miglioramento o una nuova funzionalità da implementare.
  • Scrivi il codice, testalo e invialo come pull request al repository.

Career center

Learners who complete Machine Learning e Data Mining in R will develop knowledge and skills that may be useful to these careers:
Data Scientist
A Data Scientist uses their understanding of data analysis and machine learning to extract insights and provide solutions to complex business problems. Their work helps companies improve their operations, products, and marketing strategies. They are often employed by large companies that can afford to hire a team of Data Scientists, such as technology and financial services firms. This course can help you develop the skills needed to be successful as a Data Scientist by providing a strong foundation in machine learning and data mining techniques.
Machine Learning Engineer
Machine Learning Engineers are responsible for developing, testing, and deploying machine learning models. They work closely with Data Scientists to ensure that the models are accurate and efficient. This course can help you build a strong foundation in machine learning by providing hands-on experience with a variety of techniques.
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to provide insights to businesses. They use their skills in statistics and programming to identify trends and patterns in data. This course can help you develop the skills needed to be successful as a Data Analyst by providing a strong foundation in data analysis and machine learning techniques.
Quantitative Analyst
Quantitative Analysts (also known as Quants) use mathematical and statistical models to analyze financial data and make investment decisions. They are employed by hedge funds, investment banks, and other financial institutions. This course can help you develop the skills needed to be successful as a Quantitative Analyst by providing a strong foundation in machine learning and data mining techniques.
Statistician
Statisticians collect, analyze, interpret, and present data. They use their skills to solve problems in a variety of fields, including healthcare, finance, and marketing. This course can help you develop the skills needed to be successful as a Statistician by providing a strong foundation in machine learning and data mining techniques.
Business Analyst
Business Analysts use data to help businesses make better decisions. They work with stakeholders to identify problems, develop solutions, and measure results. This course can help you develop the skills needed to be successful as a Business Analyst by providing a strong foundation in data analysis and machine learning techniques.
Software Engineer
Software Engineers design, develop, and maintain software systems. They work with a variety of programming languages and technologies to create software that meets the needs of users. This course can help you develop the skills needed to be successful as a Software Engineer by providing a strong foundation in machine learning and data mining techniques.
Market Researcher
Market Researchers collect, analyze, and interpret data to understand consumer behavior. They use their findings to develop marketing strategies and campaigns. This course can help you develop the skills needed to be successful as a Market Researcher by providing a strong foundation in machine learning and data mining techniques.
Financial Analyst
Financial Analysts use data to evaluate the financial performance of companies and make investment recommendations. They work with a variety of financial data and models to provide insights to investors and other stakeholders. This course can help you develop the skills needed to be successful as a Financial Analyst by providing a strong foundation in machine learning and data mining techniques.
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. They work with a variety of data sources to develop insurance products and pricing strategies. This course can help you develop the skills needed to be successful as an Actuary by providing a strong foundation in machine learning and data mining techniques.
Epidemiologist
Epidemiologists investigate the causes and patterns of disease in populations. They use data to track outbreaks, identify risk factors, and develop prevention strategies. This course can help you develop the skills needed to be successful as an Epidemiologist by providing a strong foundation in machine learning and data mining techniques.
Biostatistician
Biostatisticians use statistical methods to analyze data in the field of biology. They work with a variety of data sources to develop models and hypotheses, and to provide insights into biological processes. This course can help you develop the skills needed to be successful as a Biostatistician by providing a strong foundation in machine learning and data mining techniques.
Health Data Analyst
Health Data Analysts use data to improve the quality and efficiency of healthcare delivery. They work with a variety of data sources to identify trends, patterns, and opportunities for improvement. This course can help you develop the skills needed to be successful as a Health Data Analyst by providing a strong foundation in machine learning and data mining techniques.
Data Engineer
Data Engineers design and build the systems that store and process large amounts of data. They work with a variety of data sources and technologies to ensure that data is available and accessible to users. This course may be useful for developing the skills needed to be successful as a Data Engineer by providing a foundation in data analysis and machine learning techniques.
Database Administrator
Database Administrators maintain and optimize databases. They work with a variety of database technologies to ensure that data is secure and accessible to users. This course may be useful for developing the skills needed to be successful as a Database Administrator by providing a foundation in data analysis and machine learning techniques.

Reading list

We've selected 15 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Machine Learning e Data Mining in R.
Classic in the field of machine learning. It provides a comprehensive overview of statistical learning methods, including linear regression, logistic regression, and tree-based methods.
Provides a comprehensive overview of pattern recognition and machine learning. It covers a wide range of topics, including supervised and unsupervised learning, feature engineering, and model evaluation.
Comprehensive introduction to the R programming language, covering data manipulation, visualization, and statistical modeling. It valuable resource for anyone new to R or looking to improve their skills.
Provides a comprehensive overview of probabilistic graphical models. It covers a wide range of topics, including Bayesian networks, Markov networks, and latent variable models.
Provides a comprehensive overview of statistical learning. It covers a wide range of topics, including supervised and unsupervised learning, feature engineering, and model evaluation.
Provides a hands-on introduction to machine learning using R. It covers a wide range of topics, including supervised and unsupervised learning, feature engineering, and model evaluation.
Provides a practical introduction to machine learning. It covers a wide range of topics, including supervised and unsupervised learning, feature engineering, and model evaluation. It is written in a clear and concise style, making it accessible to readers of all levels.
Provides a comprehensive overview of machine learning and deep learning using Python. It covers a wide range of topics, including supervised and unsupervised learning, feature engineering, and model evaluation.
Comprehensive introduction to machine learning. It covers a wide range of topics, including supervised and unsupervised learning, feature engineering, and model evaluation. It is written in a clear and concise style, making it accessible to readers of all levels.
Provides a comprehensive overview of interpretable machine learning. It covers a wide range of topics, including model interpretability, feature importance, and model debugging.
Provides a comprehensive overview of data mining using R. It covers a wide range of topics, including data preprocessing, feature engineering, and model evaluation.
Provides a gentle introduction to machine learning. It covers a wide range of topics, including supervised and unsupervised learning, feature engineering, and model evaluation. It is written in a clear and concise style, making it accessible to readers of all levels.
Provides a comprehensive overview of deep reinforcement learning. It covers a wide range of topics, including deep learning, reinforcement learning, and deep reinforcement learning algorithms.
Introduces deep learning concepts and techniques using R. It covers topics such as convolutional neural networks, recurrent neural networks, and deep reinforcement learning.

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