Machine Learning (ML), is a process of teaching an algorithm to learn. Algorithms try to find patterns from data to generalise a rule or relation to predict future unseen instances. Some of the popular results of machine learning systems are Google Translate, YouTube’s Video Recommendation System, Movie Recommendation System. The aim of the thesis was to introduce the readers the concept of ML and the phases of the ML model development process as well as their implementation in the Python programming language. Different categories of ML with examples, typical phases in an ML modelling have been explained in theory in the thesis. In addition, pre-processing data, training and testing models, optimization of a model in the supervised machine learning have been further elaborated using Linear Regression and K-Nearest Neighbor (KNN) as examples in Python programming language. The paper, as a result, can act as a starting tool and conventional guide to a beginner ML practitioner for any ML algorithms other than presented in the paper. People with knowledge of Python can further benefit from the conventions and alternatives presented in the coding section of the paper.