Machine Learning: Difference between revisions

From Chorke Wiki
Jump to navigation Jump to search
 
(16 intermediate revisions by the same user not shown)
Line 1: Line 1:
Arthur Samuel coined the term '''Machine Learning''' in 1959 and defined it as a '''Field of study that gives computers the capability to learn without being explicitly programmed'''. And that was the beginning of '''Machine Learning'''! In modern times, Machine Learning is one of the most popular (if not the most!) career choices. According to [https://www.indeed.com/lead/best-jobs-2019 Indeed], '''Machine Learning Engineer''' Is '''The Best Job of 2019''' with a 344% growth and an average base salary of '''$146,085''' per year.
Arthur Samuel coined the term '''Machine Learning''' in 1959 and defined it as a '''Field of study that gives computers the capability to learn without being explicitly programmed'''. And that was the beginning of '''Machine Learning'''! In modern times, Machine Learning is one of the most popular (if not the most!) career choices. According to [https://www.indeed.com/lead/best-jobs-2019 Indeed], '''Machine Learning Engineer''' Is '''The Best Job of 2019''' with a 344% growth and an average base salary of '''$146,085''' per year.
{|
|valign='top'|
<source lang="xml">
<dependency>
    <groupId>de.sciss</groupId>
    <artifactId>sphinx4-data</artifactId>
    <version>1.0.0</version>
</dependency>
</source>
|valign='top'|
<source lang="xml">
<dependency>
    <groupId>net.sf.phat</groupId>
    <artifactId>sphinx4-core</artifactId>
    <version>5prealpha</version>
</dependency>
</source>
|}


==Prerequisites==
==Prerequisites==
* Linear Algebra and Multivariate Calculus
* Multivariate Calculus
* Linear Algebra
* Statistics
* Statistics
* Python
* Python


==Various ML Concepts==
==Concepts==
* Terminologies of ML
* Types
* Types of ML
* Practices
* Practice ML
* Terminologies
* Learning Resources for ML
* Learning Resources
* Take part in Competitions
* Take part in Competitions


==Types of Machine Learning==
==Types==
* '''Supervised Learning:''' This involves learning from a training dataset with labeled data using classification and regression models. This learning process continues until the required level of performance is achieved.
* Supervised Learning
* '''Unsupervised Learning:''' This involves using unlabelled data and then finding the underlying structure in the data in order to learn more and more about the data itself using factor and cluster analysis models.
* Unsupervised Learning
* '''Semi-supervised Learning:''' This involves using unlabelled data like Unsupervised Learning with a small amount of labeled data. Using labeled data vastly increases the learning accuracy and is also more cost-effective than Supervised Learning.
* Semi-supervised Learning
* '''Reinforcement Learning:''' This involves learning optimal actions through trial and error. So the next action is decided by learning behaviors that are based on the current state and that will maximize the reward in the future.
* Reinforcement Learning
 
==Terminologies==
* Model
* Feature
* Target (Label)
* Training
* Prediction
 
==References==
{|
| valign="top" |
* [https://mvnrepository.com/artifact/de.sciss/sphinx4-data MVN Repository <code>de.sciss/sphinx4-data</code>]
* [https://medium.com/skyline-ai/jupyter-notebook-is-the-cancer-of-ml-engineering-70b98685ee71 Jupyter Notebook is the Cancer of ML]
* [https://www.geeksforgeeks.org/how-to-start-learning-machine-learning/ Start Learning Machine Learning]
* [https://mvnrepository.com/artifact/net.sf.phat MVN Repository <code>net.sf.phat</code>]
* [https://diceus.com/python-vs-java-for-big-data/ Python or Java  for Big Data]
* [https://cmusphinx.github.io/ CMUSphinx Project]
* [https://cmusphinx.github.io/wiki/ CMUSphinx Wiki]
* [[Jupyter]]
* [[Spark]]
* [[NLP]]
 
| valign="top" |
* [https://opennlp.apache.org/ Apache OpenNLP]
* [https://mahout.apache.org/ Apache Mahout]
* [https://deeplearning4j.konduit.ai/ Deeplearning4j]
* [https://github.com/langchain4j/langchain4j LangChain4J]
* [https://adams.cms.waikato.ac.nz/ ADAMS]
 
| valign="top" |
 
|}

Latest revision as of 08:03, 17 November 2023

Arthur Samuel coined the term Machine Learning in 1959 and defined it as a Field of study that gives computers the capability to learn without being explicitly programmed. And that was the beginning of Machine Learning! In modern times, Machine Learning is one of the most popular (if not the most!) career choices. According to Indeed, Machine Learning Engineer Is The Best Job of 2019 with a 344% growth and an average base salary of $146,085 per year. 


<dependency>
    <groupId>de.sciss</groupId>
    <artifactId>sphinx4-data</artifactId>
    <version>1.0.0</version>
</dependency>

<dependency>
    <groupId>net.sf.phat</groupId>
    <artifactId>sphinx4-core</artifactId>
    <version>5prealpha</version>
</dependency>

Prerequisites

  • Multivariate Calculus
  • Linear Algebra
  • Statistics
  • Python

Concepts

  • Types
  • Practices
  • Terminologies
  • Learning Resources
  • Take part in Competitions

Types

  • Supervised Learning
  • Unsupervised Learning
  • Semi-supervised Learning
  • Reinforcement Learning

Terminologies

  • Model
  • Feature
  • Target (Label)
  • Training
  • Prediction

References

Retrieved from "https://cdn.chorke.org/weke/index.php?title=Machine_Learning&oldid=10480"