1, python development environment: PyCharm
Basic library:
2、
3、
Machine learning classifier
1. supervised learning
Supervised learning is the use of limited training data set has been labeled by some to establish a model of learning strategies / methods to achieve labeling of new data / instances (classification) / mapping. Supervised learning classification and labeling requirements of training samples is known, the more accurate classification and labeling, the more representative sample, the higher the accuracy of the learning model. Supervised learning in natural language processing, information retrieval, text mining, handwriting recognition, spam detection and other fields have been widely applied.
2. Unsupervised Learning
Unsupervised learning using limited data hiding unmarked described structure / rule of unlabeled data. Unsupervised learning and training samples do not need to manually labeled data, ease of compressed data storage, reducing the amount of calculation algorithms to enhance the speed, also avoid misclassification problem caused by offset positive and negative samples, mainly for economic forecasting, anomaly detection, data mining , image processing, pattern recognition, such as organizing large-scale computer clusters, social network analysis, market segmentation, astronomical data analysis.
3. semi-supervised learning
Semi-supervised learning between supervised learning and unsupervised learning, the main problem is to use a small amount of labeled samples and a large number of unlabeled samples for training and classification, so as to reduce the cost of labeling, the purpose of improving learning ability.
4. Reinforcement Learning
Reinforcement learning is an intelligent system mapping behavior from the environment to learn in order to strengthen the function of the signal value of the maximum. As the external environment provided little information, learning reinforcement learning system must rely on their own experience. Strengthen the goal of learning is to learn from the environment mapping state to act, so that the behavior of the agent selected for its ability to get the most reward environment, so that the external environment was evaluated as the best in some sense the learning system. In its robot control, unmanned, chess, industrial control and other fields successfully applied.