Artificial intelligence and machine learning are two of the most popular buzzwords in the market and many times are used interchangeably. They have become a part of everyday life, but that does not mean we understand them well. Lot of confusion exists between what is machine learning and what is AI. in most companies; marketing overlooks this distinction for advertising and sales. As technology, and, importantly, our understanding of how our minds work, has progressed, our concept of what constitutes AI has changed. Rather than increasingly complex calculations, work in the field of AI concentrated on mimicking human decision making processes and carrying out tasks in ever more human ways.
Algorithms that parse data, learn from that data, and then apply what they've learned to make informed decisions. I'm sure you are asking yourself, how can a program or algorithm make decisions and learn from data, doesn't every program need to be programmed? Not if the program was trained to learn from and adapt to data. In the case of machine learning the algorithm is not explicitly programmed, rather the model is "trained" using historical and present data in order to make future decisions and prediction. The more data available for training, the more accurate the predictions are.
A reported weakness of C4.5 in domains with continuous attributes is addressed by modifying the formation and evaluation of tests on continuous attributes. An MDL-inspired penalty is applied to such tests, eliminating some of them from consideration and altering the relative desirability of all tests. Empirical trials show that the modifications lead to smaller decision trees with higher predictive accuracies. Results also confirm that a new version of C4.5 incorporating these changes is superior to recent approaches that use global discretization and that construct small trees with multi-interval splits.