Machine Learning: Difference between revisions

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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==
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==Concepts==
==Concepts==
* Types
* Practices
* Terminologies
* Terminologies
* Types
* Practicing
* Learning Resources
* Learning Resources
* Take part in Competitions
* Take part in Competitions
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* Semi-supervised Learning
* Semi-supervised Learning
* Reinforcement Learning
* Reinforcement Learning
==Terminologies==
* Model
* Feature
* Target (Label)
* Training
* Prediction


==References==
==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://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 09:03, 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