An Introduction to Machine Learning

Why Machine Learning? Using hand-coded rules to make decisions has major disadvantages: The logic required to make a decision is specific to a single domain and task. Changing the task even slightly might require a rewrite of the whole system. Designing rules requires a deep understanding of how a decision should be made by a human expert. Problems Machine Learning Can Solve Supervised learning: Automate decision-making processes by generalizing from known examples the user provides the algorithm with pairs of inputs and desired outputs, and the algorithm finds a way to produce the desired output given an input. the algorithm is able to create an output for an input it has never seen before! Unsupervised Learning In unsupervised learning, only the input data is known, and no known output data is given to the algorithm. Examples: Identifying topics in a set of blog posts Detecting abnormal access patterns to a website Representation of Data Each entity (row) here is known as a sample (or data point), while the columns—the properties that describe these entities—are called features. Knowing Your Task and Knowing Your Data Most important part in the machine learning process is Understanding the data you are working with how it relates to the task you want to solve.