What is machine learning?
The definition of machine learning
Machine learning is an area of artificial intelligence (AI) that empowers computers to “learn” from data without being explicitly programmed to do so. Using algorithms, machine learning systems analyze data in a repetitive manner, identifying patterns within datasets and using these to make predictions or decisions.
The three main types of machine learning are:
- Supervised learning: Models are trained using labeled data (a model can access labels/details of previous outcomes and use these as reference points to validate new outcomes).
- Unsupervised learning: Models are trained using unlabeled data (so have no reference points, are identifying data patterns from scratch).
- Reinforcement learning: Based on trial and error, a model is rewarded for desired outcomes and thereby learns which actions should be taken.
Machine learning mimics human learning in that the model’s knowledge improves over time. The ultimate goal is for machines to improve their performance as they learn, dynamically adapting to new information. This ability makes them capable of handling a wide variety of tasks, ranging from basic image and language processing to complex decision making.
Machine learning outcomes/results vary greatly in terms of type and practical purpose. Many are encountered in day-to-day life, including facial recognition, product recommendations, and email spam filtering.
Deploying a machine learning model
Building and optimizing a machine learning model requires machine learning engineers with specialist programming skills and can require a significant investment of resources and time. This initial outlay is rewarded after the deployment of a model, when little human intervention is required to achieve impressive results.
Once data has been collected and prepared (cleansed to remove data that is incorrect, incomplete, duplicated, etc.), model building and rigorous evaluation can begin. When a model is deployed, continuous tracking of its outcomes allows the machine learning engineer to dynamically update and improve the model.
Machine learning and marketing
When it comes to marketing, machine learning holds vast potential. From segmenting your customer base to predicting future consumer behavior and identifying fraud, machine learning models are already embedded in day-to-day marketing practices, and future possibilities seem boundless.
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