Oulu University of Applied Sciences
Infromation Technology
Author: Nibesh Khadka
Title of the bachelor’s thesis: General Machine Learning Practices Using Py-thon
Supervisor: Kari Jyrkkä
Term and year of completion: Spring, 2019 Number of pages: 73
Machine Learning (ML), is a process of teaching an algorithm to learn, feeding data. 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 learn-ing 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 pa-per. People with knowledge of Python can further benefit from the conventions and alternatives presented in the coding section of the paper.
Keywords: Machine Learning, ML Phases, Supervised Machine Learning, Py-thon Programming, Linear Regression, KNN
