Machine Learning is a Learning of computer algorithms that machine improves automatically through past experience. It is a subset of Artificial Intelligence. Machine learning algorithms build a mathematical model based on past data and make predictions or decisions.
Machine Learning is based on a model of human brain cell interaction.The model was created in 1949
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Why Machine Learning?
The limitless quantity of available data, affordable data storage, and the growth of less expensive and more powerful processing has propelled the growth of machine learning. Many industries are developing more robust machine learning models capable of more complex data and analyzing bigger while delivering faster, more accurate results on vast scales. Machine learning tools enable organizations to more quickly identify profitable opportunities and risk.
Who uses Machine Learning?
The current time many companies are using machine learning like Google, Netflix, Amazon, Twitter, Pinterest, NASA, IBM, Facebook etc.
Application of Machine Learning
The Machine Learning are using in Banking, Finance, Manufacturing, Transport, Healthcare, E-Commerce etc.
- Banking and finance:
The financial institutions are aware of Armed and Information is the 21st Century gold, with machine learning and artificial intelligence technologies, they have the opportunity to analyze data that originates beyond the bank office. Companies of financial store and collect more user data in order to revise their strategies, prevent fraud, improve user experienceand mitigate risk. In this Machine Learning, we will talk about Artificial Intelligence and Machine Learning are used as well as the benefits and risks .
- Healthcare:
Machine learning in Google has developed a machine learning algorithm to help identify cancerous tumors on mammograms. The deep learning algorithm to identify skin cancer in healthcare.The machine-learning algorithm that was able to diagnose diabetic. It’s clear that machine learning puts another arrow in the quiver of clinical prediction.
Types of Machine Learning.
Supervise Learning:
In Supervised learning you have input variables x and anoutput variable Y and you use an algorithm to learn the mapping function fromthe input to theoutput Supervised learning problems can be further divide into two parts regression and classification
problems.
Classification:
A classification problem is when the output variable is a category, such as “red” or “blue” or “disease” and “no disease”.
Regression:
A regression problem is when the output variable is a real value, such as “dollars” or “weight”.
- Unsupervised Learning:
In Unsupervised learning youhave only input data (X) and no
corresponding output variables.Unsupervised learning problems can be further devideinto two groupsclustering and association problems.
Clustering:
In Clustering you have input data X andbut You have not output data Y so you need to create output Y then predict.
Association:
In Association you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y.
3. Semi-Supervised Machine Learning:
Supposeyou have a large amount of input data X and only some of the
data is labeled Y are called semi supervised learning Techniques. These problems create in between both unsupervised and supervised learning.
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