In general, machine learning can be divided into the following categories.
-
Supervised learning: the learned samples have already performed labeling, have corresponding labels, and need to be predicted for the labels of the new sample data. If the labels to be predicted are discrete, this is a classification algorithm, such as predicting whether watermelon is tasty or not, detecting spam, recognizing handwritten numbers, predicting weather clouds and sunshine, etc. If the labels to be predicted are continuous real numbers, this is a regression algorithm, such as predicting temperature, predicting disk capacity, etc.
-
Unsupervised learning: the learning samples are only data without corresponding labels, which is equivalent to self-learning without a teacher. Typical unsupervised learning are clustering algorithms and dimensionality reduction algorithms. The common clustering algorithms are center-of-mass-based clustering, probability distribution-based clustering, density-based clustering, graph-based clustering, etc. And dimensionality reduction algorithms are used to reduce the dimensionality of data for computation or storage.
-
Deep learning: It is based on deep neural networks. There is deep learning, of course, there is shallow learning, that is, the early ANN is also called shallow neural network. With the development of technology and business needs, the implicit layer of neural network becomes deeper and deeper, so it evolves into deep neural network. Deep learning has been a great success in image recognition, machine vision, speech recognition, sentiment analysis, etc.
-
Reinforcement learning: also known as reactive learning, evaluative learning or augmented learning, is typically characterized by feedback-based interaction, continuous reinforcement, and continuous improvement. Typical applications include gaming, gaming and robotics.
-
Integrated learning: It is mostly used in classification scenarios, where several weak classifiers collaborate to become a strong classifier, so it can be compared to three stinkers topping one Zhuge Liang. Random forest is a typical integrated learning algorithm.
There will be a series of practices to analyze machine learning algorithms one by one, so stay tuned.