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Introduction to clustering models by using R and Tidymodels - part 4 of 4

Microsoft Reactor

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Introduction to clustering models by using R and Tidymodels - part 4 of 4

This is the fourth and final episode of - An introduction to R and Machine learning. What is the session about? In this session you'll learn: • When to use clustering models • How to train and evaluate clustering models using the Tidymodels framework Who is it aimed at? This session is aimed at anyone who would like to get started with data science in R Why should you attend? Get an introduction to clustering models and learn how to train a clustering model in R Any pre-requisites? • Knowledge of basic mathematics • Some experience programming in R Speaker Bio's Carlotta Castellucio – Cloud Advocate, Microsoft Carlotta Castelluccio is a Cloud Advocate at Microsoft, focused on Data Analytics and Data Science. As a member of the Developer Relationships Academic team, she works on skilling and engaging educational communities to create and grow with Azure Cloud, by contributing to technical learning content and supporting students and educators in their learning journey with Microsoft technologies. Before joining the Cloud Advocacy team, she have been working as an Azure and AI consultant in Microsoft Industry Solutions team, mainly involved in customer-face engagements focused on Conversational AI solutions. Carlotta earned her Master’s Degree in Computer Engineering from Politecnico di Torino and her Diplôme d'ingénieur from Télécom ParisTech, by completing a E+/EU Double Degree Program. Eric is an Early Career Researcher who continually seeks to tackle real-world challenges using applied research, data analytics and machine learning; all wrapped in unbridled empathy and enthusiasm. He is currently a Data Scientist/Researcher at the Leeds Institute for Data Analytics (LIDA) in the University of Leeds, working on the British Academy project undertaking urban transport modelling in Hanoi. He has also done research in robotics, computer vision and speech processing in Japan and Kenya, aimed at creating safe working environments and exploring human-robot interaction in board games. Eric holds a BSc in Electrical and Electronic Engineering (2021) from Dedan Kimathi University of Technology Kenya. He plays the guitar (terribly but passionately). EU Double Degree Program. [eventID:15911]

Microsoft Reactor

114K subscribers

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Introduction to clustering models by using R and Tidymodels - part 4 of 4

Key moments View all

Learning Objectives

Learning Objectives

Learning Objectives

Introduction of Clustering

Introduction of Clustering

Introduction of Clustering

Clustering Is a Form of Unsupervised Machine Learning

Clustering Is a Form of Unsupervised Machine Learning

Clustering Is a Form of Unsupervised Machine Learning

Clustering Algorithms

Clustering Algorithms

Clustering Algorithms

Hierarchical Clustering

Hierarchical Clustering

Hierarchical Clustering

Measure the Dissimilarity

Measure the Dissimilarity

Measure the Dissimilarity

Determine the Optimal Number of Clusters

Determine the Optimal Number of Clusters

Determine the Optimal Number of Clusters

The Clustering Challenge

The Clustering Challenge

The Clustering Challenge

Microsoft Reactor

Introduction to clustering models by using R and Tidymodels - part 4 of 4

Key moments

Learning Objectives

Learning Objectives

Learning Objectives

Introduction of Clustering

Introduction of Clustering

Introduction of Clustering

Clustering Is a Form of Unsupervised Machine Learning

Clustering Is a Form of Unsupervised Machine Learning

Clustering Is a Form of Unsupervised Machine Learning

Clustering Algorithms

Clustering Algorithms

Clustering Algorithms

Hierarchical Clustering

Hierarchical Clustering

Hierarchical Clustering

Measure the Dissimilarity

Measure the Dissimilarity

Measure the Dissimilarity

Determine the Optimal Number of Clusters

Determine the Optimal Number of Clusters

Determine the Optimal Number of Clusters

The Clustering Challenge

The Clustering Challenge

The Clustering Challenge

The Principal Component Analysis

The Principal Component Analysis

The Principal Component Analysis

Recipe Object

Recipe Object

Recipe Object

Recipe for Clustering Algorithm

Recipe for Clustering Algorithm

Recipe for Clustering Algorithm

Normalizing the Predictors

Normalizing the Predictors

Normalizing the Predictors

Principal Component Analysis

Principal Component Analysis

Principal Component Analysis

Computing the Number of Clusters

Computing the Number of Clusters

Computing the Number of Clusters

Prepping and Baking Our Training Data Set

Prepping and Baking Our Training Data Set

Prepping and Baking Our Training Data Set

Creating a Table

Creating a Table

Creating a Table

Extract the Model from the Model Column

Extract the Model from the Model Column

Extract the Model from the Model Column

Key moments

Key moments