Sunday, February 28, 2021
  • Setup menu at Appearance » Menus and assign menu to Top Bar Navigation
Advertisement
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News
No Result
View All Result
NikolaNews
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News
No Result
View All Result
NikolaNews
No Result
View All Result
Home Neural Networks

Explain learning about the machine to my mom [ who does not have a background in STEM ].

February 4, 2020
in Neural Networks
Explain learning about the machine to my mom [ who does not have a background in STEM ].
597
SHARES
3.3k
VIEWS
Share on FacebookShare on Twitter

Machine Learning is divided into two main areas: supervised learning and unsupervised learning. Although it may seem that the first refers to prediction with human intervention and the second does not, these two concepts have more to do with what we want to do with the data.

Supervised learning
The machine is provided with detailed and labeled data and information. This is the knowledge base for your analyses. The different examples serve you to make further generalizations.

You might also like

How AI Can Be Used in Agriculture Sector for Higher Productivity? | by ANOLYTICS

Future Tech: Artificial Intelligence and the Singularity | by Jason Sherman | Feb, 2021

Tackling ethics in AI algorithms: the case of Salesforce | by Iflexion | Feb, 2021

Unsupervised learning
It’s more like the way our brain works. The computer receives no prior information about the data. It has to track its database and establish patterns through understanding and abstraction.

Learning by reinforcement
The computer learns from experience. It is based on the trial-and-error system, by which the observation of the world around it is the basis of its learning. That feedback makes it better.

Linear Regression and Logistic Regression (Supervised Learning)
Linear Regression is a Machine Learning algorithm used to obtain a numerical result. This algorithm tries to establish a linear relationship between independent variables and output or dependent variable. An example of the application of Linear Regression, shown in Figure, would be the prediction of the demand for a product at a given time from a set of previously recorded demand data. Logistic Regression, on the other hand, is not used for numerical variables but is used to predict the outcome of a categorical variable as a function of independent variables.

Linear Regression

An example of the application of logistic regression is to predict whether a customer will give up a certain service from a telephone company.

Decision Tree (Supervised Learning)
Decision tree models where the target variable can take a finite set of values are called classification trees. A Decision Tree can be used, for example, to produce a model for medical diagnostics.

Decision Tree

Boosted Decision Tree and Decision Forest (Supervised Learning)
The Boosted Decision Tree algorithm, whose operation is schematically illustrated in Figure, is based on a set of Decision Trees in which the second tree corrects the errors of the first, the third corrects the errors of the first and second and so on. On the other hand, a Decision Forest works by building multiple Decision Trees and “voting” for the most popular type of output. Therefore, unlike the Boosted Decision Tree, where the results are additive, in a Decision Forest, the results are averaged. The Boosted Decision Tree and Decision Forest algorithms are used to study the same type of problems that are analyzed with Decision Trees.

Boosted Decision Tree

Neural Network and Averaged Perceptron (Supervised Learning)
A Neural Network is composed of a set of interconnected layers in which the input values or inputs give rise to the output values or outputs through a series of nodes with their corresponding weights. Between the input and output layers, there may be one or more hidden layers, as shown in Figure 5. The Averaged Perceptron is a simplified version of the Neural Network that classifies the inputs into the different possible outputs based on a linear function.

Neural Network

Clustering Algorithms (Unsupervised learning)
They classify the data in groups or clusters according to the similarities of their attributes. At the same time, they look for data grouped in different clusters to be different. To assess the differences between the data, the clustering algorithms calculate the Euclidean distance between numerical attributes, so that the lower this value is, the more similar the instances are and the more likely they are to be grouped in the same cluster.

Clustering Algorithms

Anomaly Detection Algorithms (Unsupervised learning)
One of the most used algorithms in Anomaly Detection is the Isolation Forest algorithm, in which very anomalous instances will present very different attributes from the usual ones, which will allow us to differentiate and separate them from the rest that compose the data set. By establishing successive conditions on the attributes, the instances are separated in nodes. Anomaly Detection algorithms are often applied to detect fraud, for example in cases of bank loans.

Classifier comparison
A comparison of various classifiers in scikit-learn into synthetic data sets. The purpose of this example is to illustrate the nature of the decision boundaries of the different classifiers. Particularly in high dimensional spaces, data can be more easily separated linearly and the simplicity of classifiers such as naive Bayes and linear SVM could lead to better generalization than other classifiers.

Let’s play Machine Learning

The price of a house:

The price could be:

  • COP 80.000
  • COP 120.000
  • COP 190.000

The price is 💁 $120,000

Is machine learning magic?

Once you realize how easy it is to apply machine learning techniques to seemingly difficult problems (such as handwriting recognition), you begin to feel that you can use machine learning to solve any problem and get a satisfactory answer as long as you have enough data. Just feed the data and watch the computer magically find the answer!. But it is important to remember that machine learning only works if the problem can be solved first with the data you have.

How to learn more about machine learning?

If you want to learn about this wonderful world, I leave you this repository where you will find a list of courses and materials about ML and others issues.

References.

http://slides.com/marisbotero/deck#

Kaggle: Perros vs Gatos, Clasificación de Imágenes usando Redes Convolucionales from Emilio Garcia

https://es.wikipedia.org/wiki/Aprendizaje_autom%C3%A1tico

https://www.bbva.com/es/machine-learning-que-es-y-como-funciona/

¿Qué es Machine Learning?

Algoritmos de entrenamiento en Machine Learning

Credit: BecomingHuman By: Calypso Brontë

Previous Post

Guess what? GDPR enforcement is on fire!

Next Post

Making 3-D Printing Smarter With Machine Learning - USC Viterbi

Related Posts

How AI Can Be Used in Agriculture Sector for Higher Productivity? | by ANOLYTICS
Neural Networks

How AI Can Be Used in Agriculture Sector for Higher Productivity? | by ANOLYTICS

February 27, 2021
Future Tech: Artificial Intelligence and the Singularity | by Jason Sherman | Feb, 2021
Neural Networks

Future Tech: Artificial Intelligence and the Singularity | by Jason Sherman | Feb, 2021

February 27, 2021
Tackling ethics in AI algorithms: the case of Salesforce | by Iflexion | Feb, 2021
Neural Networks

Tackling ethics in AI algorithms: the case of Salesforce | by Iflexion | Feb, 2021

February 27, 2021
Creative Destruction and Godlike Technology in the 21st Century | by Madhav Kunal
Neural Networks

Creative Destruction and Godlike Technology in the 21st Century | by Madhav Kunal

February 26, 2021
How 3D Cuboid Annotation Service is better than free Tool? | by ANOLYTICS
Neural Networks

How 3D Cuboid Annotation Service is better than free Tool? | by ANOLYTICS

February 26, 2021
Next Post
Making 3-D Printing Smarter With Machine Learning – USC Viterbi

Making 3-D Printing Smarter With Machine Learning - USC Viterbi

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Recommended

Plasticity in Deep Learning: Dynamic Adaptations for AI Self-Driving Cars

Plasticity in Deep Learning: Dynamic Adaptations for AI Self-Driving Cars

January 6, 2019
Microsoft, Google Use Artificial Intelligence to Fight Hackers

Microsoft, Google Use Artificial Intelligence to Fight Hackers

January 6, 2019

Categories

  • Artificial Intelligence
  • Big Data
  • Blockchain
  • Crypto News
  • Data Science
  • Digital Marketing
  • Internet Privacy
  • Internet Security
  • Learn to Code
  • Machine Learning
  • Marketing Technology
  • Neural Networks
  • Technology Companies

Don't miss it

Why would you ever trust Amazon’s Alexa after this?
Internet Security

Why would you ever trust Amazon’s Alexa after this?

February 28, 2021
AI & ML Are Not Same. Here's Why – Analytics India Magazine
Machine Learning

AI & ML Are Not Same. Here's Why – Analytics India Magazine

February 27, 2021
Microsoft: We’ve open-sourced this tool we used to hunt for code by SolarWinds hackers
Internet Security

Microsoft: We’ve open-sourced this tool we used to hunt for code by SolarWinds hackers

February 27, 2021
Is Wattpad and its machine learning tool the future of TV? — Quartz
Machine Learning

Is Wattpad and its machine learning tool the future of TV? — Quartz

February 27, 2021
Oxford University lab with COVID-19 research links targeted by hackers
Internet Security

Oxford University lab with COVID-19 research links targeted by hackers

February 27, 2021
The Education Industrial Complex: The Hammer We Have
Data Science

The Education Industrial Complex: The Hammer We Have

February 27, 2021
NikolaNews

NikolaNews.com is an online News Portal which aims to share news about blockchain, AI, Big Data, and Data Privacy and more!

What’s New Here?

  • Why would you ever trust Amazon’s Alexa after this? February 28, 2021
  • AI & ML Are Not Same. Here's Why – Analytics India Magazine February 27, 2021
  • Microsoft: We’ve open-sourced this tool we used to hunt for code by SolarWinds hackers February 27, 2021
  • Is Wattpad and its machine learning tool the future of TV? — Quartz February 27, 2021

Subscribe to get more!

© 2019 NikolaNews.com - Global Tech Updates

No Result
View All Result
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News

© 2019 NikolaNews.com - Global Tech Updates