Course Description Machine-Learning enables us to uncover trends and patterns hidden in data and make predictions based on historical observations. Machine-Learning is crucial in implementing Artificial Intelligence (AI) systems and helps industry and academia in complex problem-solving, predictive analytics, automation, etc. Therefore, Machine-Learning is an essential skill a Data Science and related technical professionals should carry in their toolboxes. This course aims to provide a fundamental understanding of the core principles of Machine Learning (ML) with hands-on training on applying machine learning to solve real-world problems. A learner who completes this course should be able to define a machine learning problem, understand the solution path, and display the ability to carry out the end-to-end process of building a machine learning application. Topics Covered Introduction to Machine Learning (ML), History, and Applications Setting up a Computing Environment, Python and Required Libraries. Knowledge Foundations for ML (Computing, Statistics, and Mathematics) Exploratory Data Analysis (EDA) and Feature Engineering Supervised Machine Learning Unsupervised Machine Learning Explaining ML Models and Predictions Introduction to Deep Learning and Neural Networks Design, Develop and Deploy ML Solutions Capstone Project Prerequisites: Basics of computer programming, mathematics, and statistics. Basic knowledge in computer applications: spreadsheet, word processor and presentation authoring. This is the initial release of the Machine Learning Foundations Course Repository by Sumudu Tennakoon Full Changelog: https://github.com/SumuduTennakoon/MachineLearningFoundations/commits/v1.0.0