What Does ‘Machine Learning’ Mean? Slang Definition of Machine Learning

Although not all machine learning is statistically based, computational statistics is an important source of the field’s methods. Machine learning is a set of methods that computer scientists use to train computers how to learn. Instead of giving precise instructions by programming them, they give them a problem to solve and lots of examples (i.e., combinations of problem-solution) to learn from. When a problem has a lot of answers, different answers can be marked as valid.

For example, let’s say that we had a set of photos of different pets, and we wanted to categorize by “cat”, “dog”, “hamster”, et cetera. Deep learning algorithms can determine which features (e.g. ears) are most important to distinguish each animal from another. In machine learning, this hierarchy of features is established manually by a human expert. The various data applications of machine learning are formed through a complex algorithm or source the machine or computer. This programming code creates a model that identifies the data and builds predictions around the data it identifies.

Model assessments

Machine learning and deep learning models are capable of different types of learning as well, which are usually categorized as supervised learning, unsupervised learning, and reinforcement learning. Supervised learning utilizes labeled datasets to categorize or make predictions; this requires some kind of human intervention to label input data correctly. In contrast, unsupervised learning doesn’t require labeled datasets, and instead, it detects patterns in the data, clustering them by any distinguishing characteristics. Reinforcement learning is a process in which a model learns to become more accurate for performing an action in an environment based on feedback in order to maximize the reward.

Not every company has a team of skilled data scientists, or a pressing need to invest in such a team. In these cases, AutoML can be labeled a “better” fit simply because it enables organizations to do more with the in-house skills they have. Asking whether AutoML is better than human-built machine learning is like asking whether to rent a 3D printer or hire a sculptor with a master’s degree; the answer lies in what you need from the product. Rather than choosing to invest in either AutoML or data scientists, tech leaders must recognize that the future lies in both. The system used reinforcement learning to learn when to attempt an answer (or question, as it were), which square to select on the board, and how much to wager—especially on daily doubles.

Machine Learning at present:

“It may not only be more efficient and less costly to have an algorithm do this, but sometimes humans just literally are not able to do it,” he said. This has naturally prompted tech leaders and the data science community to compare AutoML to humans, asking which is better and whether data scientists will be left behind. Let’s examine why, and explore some other questions we should be asking instead. Bias and discrimination aren’t limited to the human resources function either; they can be found in a number of applications from facial recognition software to social media algorithms.

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