Machine Learning is fundamental to data-driven decision making in many fields today. This journey walks through what it is, how machine learning algorithms work and how to implement and evaluate them. The focus will be on understanding the concepts of machine learning, feature engineering and data transformations prior to implementing machine learning models. Understanding, implementing and evaluating foundational supervised learning models such as linear and logistic regression. We will learn about popular supervised algorithms such as the Naive Bayes Classifier, Support Vector Machines, K-Nearest Neighbors and Decision Trees, understanding how recommender systems work and how to build and evalute them. Mastering unsupervised learning algorithms such as Principal Component Analysis and K Means Clustering.