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. | ||
<source lang="xml"> | |||
<dependency> | |||
<groupId>de.sciss</groupId> | |||
<artifactId>sphinx4-data</artifactId> | |||
<version>1.0.0</version> | |||
</dependency> | |||
</source> | |||
==Prerequisites== | ==Prerequisites== |
Revision as of 21:05, 15 May 2022
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>
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