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”

Technology has eaten the world

  • On Friday, I looked at the top 5 quarter-end market caps for the first quarter of 2017.
  • I went back 11 years to the same day and saw that on March 31, 2006, only one out of the top 5 market caps, namely Microsoft, was a technology company.
  • The top market caps were rather diversified, including financial services, industrial groups, technology, and of course oil production giants.
  • This does not mean there will be no market crash or bubble bursting; I am just saying that these companies are generating real revenue and profit just like the old top 5 companies, even though their services are often intangible.
  • I wouldn’t be surprised to see leaders in transportation, construction, and travel among the top 10 market caps within the next decade.

A few years ago, Marc Andreessen first declared that “software is eating the world.” We have been mostly inclined to intuitively believe the trend he was referring to, but we were missing undisputable proof. Now we have quantified proof, at least for the domination of the technology as a whole if not specifically for software.
Continue reading “Technology has eaten the world”

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”

Baidu Steps up Investment in Artificial Intelligence, Acquires Raven Tech

.@BaiduResearch Steps up Investment in Artificial Intelligence #AI , Acquires Raven Tech

(Yicai Global) Feb. 16 — Chinese Internet giant Baidu Inc. [NASDAQ:BIDU] has taken another step in developing its artificial intelligence (AI) business by announcing that it will acquire Raven Tech Co, a Beijing-based AI startup.

Continue reading “Baidu Steps up Investment in Artificial Intelligence, Acquires Raven Tech”

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

  • You need to be a member of Data Science Central to add comments!
  • In machine learning, computers apply statistical learning techniques to automatically identify patterns in data.
  • 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.
  • Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.
  • There are clearly patterns in the data, but the boundaries for delineating them are not obvious.

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”