Machine learning is a concept which allows the machine to learn from the experience. In this we train our program using existing examples and data. We keep adjusting our program until our program become near to accurate.
The basic of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output while updating outputs as new data becomes available.
In machine learning we started from analyzing the data and find the patterns and adjust the program and action accordingly.
Machine learning is sub part of ‘Artificial Intelligence’ , before learning about machine learning we should know about Artificial Intelligence as well.
Artificial Intelligence can be defined as “Artificial Intelligence gives the capability to computer/machine to work like human brain”.
Deep learning is sub part of machine learning.
Machine Learning can be further divided into 3 main sub parts
- Supervised machine learning
In supervised machine learning we train our machine using know set of input/output key pair. - Unsupervised machine learning
In unsupervised machine learning the output of given inputs is not known. - Reinforcement machine learning
In unsupervised machine learning we use trial and error search and delayed reward. It interacts with its environment by producing actions and discovers errors or rewards.
Supervised machine learning, unsupervised machine learning can be further divided into sub parts, we will see these in our next articles…
http://mycloudplace.com/tokenize-of-words-and-sentences-using-nltk/
http://mycloudplace.com/natural-language-processing-an-introduction/
Pingback: Artificial Intelligence An Introduction - Mycloudplace
Pingback: Natural Language Processing An Introduction - Mycloudplace
Pingback: Types Of Supervised Learning Algorithms - Mycloudplace