Machine Learning and Techniques

Training data can be generalized and that the model can be used on new data with some accuracy.Algorithms under supervised learning are Naive Bayes, Gradient Boosting, Neural Networks.It is often used for image recognition, speech recognition and sometimes in financial analysis.UNSUPERVISED LEARNING : It does not uses output data, and can be split into different categories.Algorithms that can be used to reduce the dimensions such as PCA, LCA, Autoencoder.To detect the observations that do not follow the data set patterns.Clustering algorithms like, K-means, mixture models..They try to separate the observations in different groups.This learning is mostly used to pre-process the data, or pre-train supervised learning algorithms.REINFORCEMENT LEARNING : These algorithms can be seen as the best possible way for earning the greatest reward..Rewards can be anything from earning money, winning a game or beating opponents.This learning method follows various steps like, model (agent) will choose the action to maximize the reward based on state of environment..These actions will change the state of the model and environment..They may be interpreted to reward the model..By performing this loop, behavior of the model will be improved and the accuracy of our model will surely show some improvement.It performs well on small dynamic system and is definitely to follow for the years to come.. More details

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