While people learn quite a bit from the human experience, machines learn from following instructions. However, machines can also learn from experience which in the world of a computer means learning from previous data.
Supervised learning and unsupervised learning are two different machine learning methods. The supervised learning approach is used for most practical learning and analyzes data to produce and inferred function for mapping. The algorithm makes generalizations based on the data to understand and predict new situations. An example includes recommendations on Amazon, face recognition technology and even a robot learning to sort garbage using visual identification.
Unsupervised learning follows the process of trying to find a hidden structure within the unlabeled data. The data is clustered into different groups through portioning to try to understand the structure or distribution of data to learn more about the data. Applications can include market segmentation for targeting customers, fraud detection in banking, image segmentation and gene clustering.
Challenges for machine learning techniques include issues around volume, velocity, and variability. More specifically, there are challenges in decision making, modeling, human interactions and data-driven scalability.
There are also some situations where there is a large about of input data and only some of the data is labeled which creates a semi-supervised learning situation. An example would be where there are a lot of images, but only a few of the images are labeled. This scenario can happen given the reasonably low cost of storing unlabeled data. In this instance, unsupervised learning can help provide insight into the structure of the input variables and supervised learning can help make predictions on the unseen data.
These tools are widely accessible and straightforward for people to set up on personal computers or to create simple models to help advance business goals.
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