What Is Machine Learning?
Definition of Machine Learning
- A decision process: A recipe of calculations or other steps that takes in the data and returns a “guess” at the kind of pattern in the data your algorithm is looking to find.
- An error function: A method of measuring how good the guess was by comparing it to known examples (when they are available). Did the decision process get it right? If not, how do you quantify “how bad” the miss was?
- An updating or optimization process: Where the algorithm looks at the miss and then updates how the decision process comes to the final decision so that the next time the miss won’t be as great.
What Are Some Machine Learning Methods?
- Supervised learning: The dataset being used has been pre-labeled and classified by users to allow the algorithm to see how accurate its performance is.
- Unsupervised learning: The raw dataset being used is unlabeled and an algorithm identifies patterns and relationships within the data without help from users.
- Semisupervised learning: The dataset contains structured and unstructured data, which guide the algorithm on its way to making independent conclusions. The combination of the two data types in one training dataset allows machine learning algorithms to learn to label unlabeled data.
- Reinforcement learning: The dataset uses a “rewards/punishments” system, offering feedback to the algorithm to learn from its own experiences by trial and error.
Why Is Machine Learning Important?
- Scale of data: Companies are faced with massive volumes and varieties of data that need to be processed. Processing power is more efficient and readily available. Models that can be programmed to process data on their own, determine conclusions, and identify patterns are invaluable.
- Unexpected findings: Since machine learning algorithms update autonomously, the analytical accuracy improves with each run as it teaches itself from the datasets it analyzes. This iterative nature of learning is unique and valuable because it occurs without human intervention, providing the ability to uncover hidden insights without being specifically programmed to do so.
Who Is Using Machine Learning?
- Marketing and sales
- Financial services
- Brick-and-mortar retail
- Oil and gas