Lets get into deeper how machine learning concepts are utilized in processing the data
1. Training data
2. Supervised and unsupervised learning
3. Classifying machine learning problems and algorithms
4. Training a model
5. Testing a model
6. Using a model
we will see all those details one by one . The ML process triggers with data’s like it’s from relation database or it might be from nosql database or it would be generated from logs . The question here is ? how we are going to utilized those data’s , those raw data’s is very rarely in right shape and it would’t be processed by machine Learning . The main reason will be , there may be an duplicate of data’s or missing values , same data’s will be expressed in different ways , the data’s may be redundant.So before moving to machine learning , we have to fine tune our data’s.
One of the best example for Supervised learning will be credit card transaction fraudulent or not . Few styles of Machine Learning algorithms are Decision tree,Neural network ,Bayesian and k-means . For example, there are decision tree algorithms. There are algorithms that use neural networks, which in some ways emulate how the brain works. There are Bayesian algorithms that use Bayes’ theorem to work up probabilities. There are K-means algorithms that are used for clustering.