Algorithmic trading relies on computer programs that execute algorithms to automate some, or all, elements of a trading strategy. Algorithms are a sequence of steps or rules to achieve a goal and can take many forms. In the case of machine learning (ML), algorithms pursue the objective of learning other algorithms, namely rules, to achieve a target based on data, such as minimizing a prediction error. In this article, we have a look at use cases of ML and how it is used in algorithmic trading strategies. These algorithms encode various activities of a portfolio manager who observes market transactions and analyzes relevant data to decide on placing buy or sell orders.