A Visual Introduction to Machine Learning

A Visual Introduction to Machine Learning | #DataScience #MachineLearning #RT

  • Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.
  • Let’s say you had to determine whether a home is in San Francisco or in New York.
  • In machine learning terms, categorizing data points is a classification task.Since San Francisco is relatively hilly, the elevation of a home may be a good way to distinguish the two cities.
  • Based on the home-elevation data to the right, you could argue that a home above 240 ft should be classified as one in San Francisco.
  • The data suggests that, among homes at or below 240 ft, those that cost more than $1776 per square foot are in New York City.

This article was written by Stephanie and Tony on R2D3. 
In machine learning, computers apply statistical learning techniques to automatically identify pattern…
Continue reading “A Visual Introduction to Machine Learning”

A Visual Introduction to Machine Learning

A Visual Introduction to #MachineLearning #abdsc

  • Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.
  • Let’s say you had to determine whether a home is in San Francisco or in New York.
  • In machine learning terms, categorizing data points is a classification task.Since San Francisco is relatively hilly, the elevation of a home may be a good way to distinguish the two cities.
  • Based on the home-elevation data to the right, you could argue that a home above 240 ft should be classified as one in San Francisco.
  • The data suggests that, among homes at or below 240 ft, those that cost more than $1776 per square foot are in New York City.

This article was written by Stephanie and Tony on R2D3. 
In machine learning, computers apply statistical learning techniques to automatically identify pattern…
Continue reading “A Visual Introduction to Machine Learning”

A Visual Introduction to Machine Learning

A Visual Introduction to #MachineLearning #abdsc

  • Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.
  • Let’s say you had to determine whether a home is in San Francisco or in New York.
  • In machine learning terms, categorizing data points is a classification task.Since San Francisco is relatively hilly, the elevation of a home may be a good way to distinguish the two cities.
  • Based on the home-elevation data to the right, you could argue that a home above 240 ft should be classified as one in San Francisco.
  • The data suggests that, among homes at or below 240 ft, those that cost more than $1776 per square foot are in New York City.

This article was written by Stephanie and Tony on R2D3. 
In machine learning, computers apply statistical learning techniques to automatically identify pattern…
Continue reading “A Visual Introduction to Machine Learning”

There’s nothing artificial about intelligence

Blog: There’s nothing artificial about intelligence  #AI #technology

  • But perhaps for now we are getting ahead of ourselves perhaps we were ascribing and intelligence to our technology which at the moment does not exist.
  • Technology and artificial intelligence can and should be seen as means to an end.
  • Smart meters, data capturers, programmable devices; it seems as though everywhere we look computers are taking over our lives.
  • More importantly, does what they buy provide a genuine solution or satisfy a genuine need or is it simply the best that they can find or afford at the time.
  • And maybe our computers can churn out loyalty vouchers and incentives, but they are only responding in a set algorithm to known sets of data.

Smart meters, data capturers, programmable devices; it seems as though everywhere we look computers are taking over our lives.
Continue reading “There’s nothing artificial about intelligence”

Machine learning helps scientists discover new materials

#MachineLearning #DataScience helps scientists discover new materials

  • Researchers published their work in the journal Nature Communications last month .
  • UPI.com is your trusted source for world news, top news, science news, health news and current events.
  • LOS ALAMOS, N.M., May 9 (UPI) — Traditionally, materials scientists have used a combination of trial-and-error and intuition to discover and perfect new materials with advantageous properties.
  • To speed up the process, researchers at Los Alamos National Laboratory attempted to marry machine learning with targeted experiments.
  • “What we’ve done is show that, starting with a relatively small data set of well-controlled experiments, it is possible to iteratively guide subsequent experiments toward finding the material with the desired target,” Turab Lookman, a physicist and materials scientist at Los Alamos, said in a news release .

Read the full article, click here.


@MikeTamir: “#MachineLearning #DataScience helps scientists discover new materials”


Researchers at Los Alamos National Laboratory attempted to marry machine learning with targeted experiments to discover new materials more efficiently.


Machine learning helps scientists discover new materials