Hi all, with article i start with learning process of machine learning and AI from very beginning. Lets start from The Machine learning process:

Data Pre-processing - This is the starting phase of almost all the new models we build. it mostly has the below common processes.

• Importing the data

• Cleaning the data -> In this we take care of various things like missing data, need of normalization or standardization based on our requirement, encoding categories or labels.

• Splitting the data into training and test sets -> Generally we split our data into 2 sets in which 80% is kept as training set on which our model is trained and 20% is test set on which performance or efficiency of our model is measured.

Modelling - In this actual model is build from already present models or ensemble(combination of models) and has the below stages mainly.

• Build

• Train - Our training data set is fed into the model based on which our model gets trained

• Make predictions - Based on training data we are ready to make predictions for unknown data set

Evaluation - In this step performance is measured for the model we have built and once we are satisfied with the results we make future predictions using and if not satisfied we repeat the above steps again as its mostly some models work great on some sets of data and some may not perform well .

Hope it give you simple overview of the process, see you next time.