What Is Machine Learning (ML)?
Definition of Machine Learning
- A decision process: A recipe of calculations or other steps that takes in the data and “guesses” what kind of pattern 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: A method in which the algorithm looks at the miss and then updates how the decision process comes to the final decision, so next time the miss won’t be as great.
Types of Machine Learning
- 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.
- Semi-supervised learning: The dataset contains structured and unstructured data, which guides 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.
Commonly Used Machine Learning Algorithms
What Is Deep Learning?
Machine Learning (ML) vs. Artificial Intelligence (AI)
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 a machine learning algorithm updates autonomously, 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 — in other words, machine learning algorithms can uncover hidden insights without being specifically programmed to do so.
Who Is Using Machine Learning?
- Marketing and sales
- Financial services
- Brick-and-mortar retail
- Health care
- Oil and gas