Michael Grogan has not received any gifts yet

Alex Souza liked Michael Grogan's blog post Visualizing New York City WiFi Access with K-Means Clustering

Apr 12

Michael Grogan's blog post was featured### Visualizing New York City WiFi Access with K-Means Clustering

Visualization has become a key application of data science in the telecommunications industry.Specifically, telecommunication analysis is highly dependent on the use of geospatial data. This is because telecommunication networks in themselves are geographically dispersed, and analysis of such dispersions can yield valuable insights regarding network structures, consumer demand and availability.DataTo illustrate this point, a k-means clustering algorithm is used to analyse geographical data for…See More

Feb 24

Dexter D'Silva liked Michael Grogan's blog post Image Recognition with Keras: Convolutional Neural Networks

Feb 21

Tansel Arif liked Michael Grogan's blog post Image Recognition with Keras: Convolutional Neural Networks

Feb 20

Michael Grogan's blog post was featured### Image Recognition with Keras: Convolutional Neural Networks

Image recognition and classification is a rapidly growing field in the area of machine learning. In particular, object recognition is a key feature of image classification, and the commercial implications of this are vast.For instance, image classifiers will increasingly be used to:Replace passwords with facial recognitionAllow autonomous vehicles to detect obstructionsIdentify geographical features from satellite imagery These are just a few of many examples of how image classification will…See More

Feb 18

Will Pranzini liked Michael Grogan's blog post Python: Implementing a k-means algorithm with sklearn

Jan 24

Michael Grogan posted a blog post### K-Nearest Neighbors (KNN): Solving Classification Problems

In this tutorial, we are going to use the K-Nearest Neighbors (KNN) algorithm to solve a classification problem. Firstly, what exactly do we mean by classification?Classification across a variable means that results are categorised into a particular group. e.g. classifying a fruit as either an apple or an orange.The KNN algorithm is one the most basic, yet most commonly used algorithms for solving classification problems. KNN works by seeking to minimize the distance between the test and…See More

Sep 29, 2018

Michael Grogan's blog post was featured### K-Nearest Neighbors (KNN): Solving Classification Problems

In this tutorial, we are going to use the K-Nearest Neighbors (KNN) algorithm to solve a classification problem. Firstly, what exactly do we mean by classification?Classification across a variable means that results are categorised into a particular group. e.g. classifying a fruit as either an apple or an orange.The KNN algorithm is one the most basic, yet most commonly used algorithms for solving classification problems. KNN works by seeking to minimize the distance between the test and…See More

Sep 29, 2018

Michael Grogan posted a blog post### Variance-Covariance Matrix: Stock Price Analysis in R

The purpose of a variance-covariance matrix is to illustrate the variance of a particular variable (diagonals) while covariance illustrates the covariances between the exhaustive combinations of variables.Why do we use variance-covariance matrices?A variance-covariance matrix is particularly useful when it comes to analysing the volatility between elements of a group of data. For instance, a variance-covariance matrix has particular applications when it comes to analysing portfolio returns.If…See More

Jun 30, 2018

Michael Grogan's blog post was featured### Variance-Covariance Matrix: Stock Price Analysis in R

The purpose of a variance-covariance matrix is to illustrate the variance of a particular variable (diagonals) while covariance illustrates the covariances between the exhaustive combinations of variables.Why do we use variance-covariance matrices?A variance-covariance matrix is particularly useful when it comes to analysing the volatility between elements of a group of data. For instance, a variance-covariance matrix has particular applications when it comes to analysing portfolio returns.If…See More

Jun 30, 2018

Michael Grogan commented on Michael Grogan's blog post Python: Implementing a k-means algorithm with sklearn

"Hi Bhanu,
You can find the link here with the dataset included: http://www.michaeljgrogan.com/k-means-clustering-python-sklearn/"

Jun 14, 2018

BhanuTeja Kasani commented on Michael Grogan's blog post Python: Implementing a k-means algorithm with sklearn

"Hi,
Where can I find the "sample_stocks.csv"
this link no more handles that file.(http://www.michaeljgrogan.com/kmeans-wss-clustering/)
Regards,
Bhanu."

Jun 10, 2018

Dan Butorovich commented on Michael Grogan's blog post Creating maps in R using ggplot2 and maps libraries

"In the US you can also use the zipcode package and merge the map to zipcodes from the zipcode data package which converts zips to lat./long coordinates. There is also a ggmap package that works well with map data and the results can be sent to…"

Apr 4, 2018

Ajay Sharma commented on Michael Grogan's blog post Data Cleaning and Wrangling With R

"Thanks Michael Grogan for this wonderful article, it is really helpful.
I need one more help, in " 2. Mimic VLOOKUP by using the merge functions" section - while merging two data frames by common variable, missing values (NA) values are…"

Mar 9, 2018

- My Web Site Or LinkedIn Profile
- http://www.michaeljgrogan.com

- Field of Expertise
- Data Science, Machine Learning, AI, Business Analytics, Deep Learning

- Your Job Title:
- Data Scientist and Statistician

- Interests:
- Networking, New venture, Other

Posted on February 19, 2019 at 3:44am 0 Comments 1 Like

Visualization has become a key application of data science in the telecommunications industry.

Specifically, telecommunication analysis is highly dependent on the use of geospatial data. This is because telecommunication networks in themselves are geographically dispersed, and analysis of such dispersions can yield valuable insights regarding network structures, consumer demand and availability.

To illustrate this point, a k-means clustering algorithm is used…

Posted on February 17, 2019 at 11:00am 0 Comments 2 Likes

Image recognition and classification is a rapidly growing field in the area of machine learning. In particular, object recognition is a key feature of image classification, and the commercial implications of this are vast.

For instance, image classifiers will increasingly be used to:

- Replace passwords with facial recognition
- Allow autonomous vehicles to detect obstructions
- Identify geographical features from satellite imagery

These…

Posted on September 28, 2018 at 4:50am 0 Comments 0 Likes

**In this tutorial, we are going to use the K-Nearest Neighbors (KNN) algorithm to solve a classification problem.** Firstly, what exactly do we mean by classification?

Classification across a variable means that results are categorised into a particular group. e.g. classifying a fruit as either an apple or an orange.

The KNN algorithm is one the most basic, yet most commonly used algorithms for solving classification problems. KNN works by seeking to minimize the…

ContinuePosted on June 30, 2018 at 4:30am 0 Comments 0 Likes

The purpose of a *variance-covariance matrix* is to illustrate the variance of a particular variable (diagonals) while covariance illustrates the covariances between the exhaustive combinations of variables.

A variance-covariance matrix is particularly useful when it comes to analysing the volatility between elements of a group of data. For instance, a variance-covariance matrix has particular applications when it comes to…

Continue- No comments yet!

© 2019 Data Science Central ® Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

**Technical**

- Free Books and Resources for DSC Members
- Learn Machine Learning Coding Basics in a weekend
- New Machine Learning Cheat Sheet | Old one
- Advanced Machine Learning with Basic Excel
- 12 Algorithms Every Data Scientist Should Know
- Hitchhiker's Guide to Data Science, Machine Learning, R, Python
- Visualizations: Comparing Tableau, SPSS, R, Excel, Matlab, JS, Pyth...
- How to Automatically Determine the Number of Clusters in your Data
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- Fast Combinatorial Feature Selection with New Definition of Predict...
- 10 types of regressions. Which one to use?
- 40 Techniques Used by Data Scientists
- 15 Deep Learning Tutorials
- R: a survival guide to data science with R

**Non Technical**

- Advanced Analytic Platforms - Incumbents Fall - Challengers Rise
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- How to Become a Data Scientist - On your own
- 16 analytic disciplines compared to data science
- Six categories of Data Scientists
- 21 data science systems used by Amazon to operate its business
- 24 Uses of Statistical Modeling
- 33 unusual problems that can be solved with data science
- 22 Differences Between Junior and Senior Data Scientists
- Why You Should be a Data Science Generalist - and How to Become One
- Becoming a Billionaire Data Scientist vs Struggling to Get a $100k Job
- Why do people with no experience want to become data scientists?

**Articles from top bloggers**

- Kirk Borne | Stephanie Glen | Vincent Granville
- Ajit Jaokar | Ronald van Loon | Bernard Marr
- Steve Miller | Bill Schmarzo | Bill Vorhies

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives**: 2008-2014 | 2015-2016 | 2017-2019 | Book 1 | Book 2 | More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions