Introduction:
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for
Difference between Machine learning and Artificial Intelligence. ... Machine Learning : Machine Learning is the learning in which machine can learn by its own without being explicitly programmed. It is an application of AI that provide system the ability to automatically learn and improve from experience.
there are number of ways to solve the problems, here we can discuss as
- Supervised learning
- Unsupervised learning
Supervised learning
The majority of practical machine learning uses supervised learning.
Supervised
learning is where you have input variables (x) and an output variable
(Y) and you use an algorithm to learn the mapping function from the
input to the output.
Y = f(X)
The goal is to approximate the mapping function so well that when you have new input data (x) that you can predict the output variables (Y) for that data
It
is called supervised learning because the process of an algorithm
learning from the training dataset can be thought of as a teacher
supervising the learning process. We know the correct answers, the
algorithm iteratively makes predictions on the training data and is
corrected by the teacher. Learning stops when the algorithm achieves an
acceptable level of performance.
Supervised learning problems can be further grouped into regression and classification problems.
- Image Classification/Classification: A classification problem is when the output variable is a category, such as “red” or “blue” or “disease” and “no disease”.You train with images/labels. Then in the future you give a new image expecting that the computer will recognize the new object.
- Market Prediction/Regression: A regression problem is when the output variable is a real value, such as “dollars” or “weight”. You train the computer with historical market data and ask the computer to predict the new price in the future.
Some
common types of problems built on top of classification and regression
include recommendation and time series prediction respectively.
Some popular examples of supervised machine learning algorithms are:
- Linear regression for regression problems.
- Random forest for classification and regression problems.
- Support vector machines for classification problems
Unsupervised Machine Learning
Unsupervised learning is where you only have input data (X) and no corresponding output variables. No
labels are given to the learning algorithm, leaving it on its own to
find structure in its input. It is used for clustering population in
different groups. Unsupervised learning can be a goal in itself
(discovering hidden patterns in data).
The goal for unsupervised learning is to model the underlying structure
or distribution in the data in order to learn more about the data.
These are called unsupervised learning because unlike supervised
learning above there is no correct answers and there is no teacher.
Algorithms are left to their own devises to discover and present the
interesting structure in the data.
Unsupervised learning problems can be further grouped into clustering and association problems.
- Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior.
- Association: An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y.
Some popular examples of unsupervised learning algorithms are:
- k-means for clustering problems.
- Apriori algorithm for association rule learning problems.
Semi-supervised learning: The computer is given only an incomplete training signal: a training set with some (often many) of the target outputs missing
References:
https://en.wikipedia.org/wiki/Machine_learning
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