Machine learning, a subfield of AI, has become a crucial component of developing tools and applications for data analysis and decision-making in the digital age.
Human-made brainpower, otherwise called AI, is mainly used to computerize natural intelligence, including replicating human‑like intelligence, or collective intelligence, capable of recreating the same decisions and actions that a naturally occurring intelligence would do.
We can teach a computer to learn by using data, and this process is called machine learning, which uses statistics.
Taking steps to fight overfitting is necessary to develop predictive models that make accurate predictions on new data, especially when using complex models like neural networks or decision trees.
Data is a primary component in innovation and the transformation of today’s enterprises. But developing an appropriate data strategy is not an easy task, as modernizing and optimizing data architectures requires highly skilled teams.
Modern machine learning methods have been around for more than 10 years, but now there is a trend to add machine learning to a wider range of applications including medicine, agriculture, and even IoT devices.