Machine Learning - How? What? and Why?
“Machine Learning” , a term came out of nowhere is now the most trending one. Though, one might not know what is it actually? People are bound to many examples, but the exact idea seems to be different.
People often mix up the terms Data Science, Machine Learning andArtificial Intelligence . These are different branches of study but one is related to each other and successive of one another.
Machine Learning algorithms are fed with huge amount of data known as Sample Data . Before feeding the data, the data must be cleaned and should be properly formatted. This process is known as Data Pre-processing .
A Machine Learning Model can never make 100% accurate predictions otherwise there is no point of training the Model.
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3 Categories of Machine Learning
- Supervised learning: It has a set of labelled data to train the model on. In a way, it means supervising a machine by providing a ton of information about a particular case and giving it the case outcome.
- Unsupervised learning:
Effective machine learning algorithms
Some of the most effective machine learning algorithms beyond deep learning include:
- ‘random forests’ that create multitudes of decision trees to optimise a prediction;
- ‘Bayesian networks’ that use a probabilistic approach to analyse variables ...
Machine Learning Explained
Machine Learning is the process of letting your machine use the data to learn the relationship between predictor variables and the target variable. It is one of the first steps toward becoming a data scientist.
There are two kinds of Machine Learning: supervised, and unsupervised learning....
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