With the development of the Internet and technology, artificial intelligence has gradually become an important direction of future technological development. In today's era of big data, technologies for data collection, mining, and application have attracted more and more attention. So in the process of learning artificial intelligence and big data development training courses, what are the special points that need to be paid attention to? Today, Xiaobian will take you to understand the three main points first.
Point 1: Data is not everything
Fundamentally, machine learning algorithms are not magic. They need to start with training data and gradually extend to unknown data. For example, assuming that you already have some understanding of the distribution of data, it can be very effective to express this prior knowledge through a graphical model. In addition to the data, you also need to carefully consider what knowledge in the field can be applied, which can be very helpful in developing a more effective classifier. The combination of data and industry experience can often do more with less.
Point 2: Generalization is the goal
A common misunderstanding in machine learning practice is getting caught up in the details and forgetting the original goal of investigating a solution to a problem.
The testing phase is a key link to verify whether a method has generalization ability (through cross-validation, external data validation, etc.), but it is not easy to find a suitable validation dataset. Trying to get good accuracy when training a model with millions of dimensional features on a set of only a few hundred samples is absurd.
Point 3: Correlation is not the same as causation
This point is worth repeating, and we can explain it with a joke: "Global warming, earthquakes, tornadoes, and other natural disasters are directly related to the decline in the number of pirates worldwide since the 18th century". The changes of these two variables are correlated, but it cannot be said that there is a causal relationship, because there is often a third category (or even category 4, 5) unobserved variables at work. Correlation should be seen as some measure of underlying causality, but further research is needed.
To learn the big data artificial intelligence development course, you must have both majors and skills! The big data artificial intelligence course uses professional courses to create a professional you. If you are interested, you can contact the editor. Preferential benefits and video materials are all available!