Machine Learning is nothing but we are using machines to predict results based on incoming data. Data is very precious these days. More the number of data the more options we have for trying out various things. Companies like google and facebook are very valuable these days because they are the only one that can collect huge volumes of data on a daily basis. Therefore, Machine Learning is pioneered by these companies that has huge volumes of data. Most companies do not have large data to make machine learning worth their time and effort. You need huge data to implement Machine Learning effectively. Big companies usually share their model but keep their data private. That’s what gives these companies a competitive advantage over the others.

Steps in Machine Learning :

1 Define the Goals [ what do we want to accomplish] 2.Data Gathering [the hardest part to do. More the data the more options we have.] 3.Data Parsing [Cleaning and Spliting Data into Training sets (80%) and Testing sets(20%)] 4.Model creation [Model 1, Model 2, Model 3] 5.Accuracy Testing (testing based on the testing sets and check how accurate the model is and whether it complies with our goals) 6 Improve the process [try for improved model]

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