Learn Machine Learning Algorithms

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In the past decade, there has been a surge in interest in machine learning. Almost every day, we can see discussions about machine learning in a variety of computer science courses, industry conferences, the Wall Street Journal, and more.

Machine learning is the most advanced aspect of the field of artificial intelligence today, and more beginners have begun to enter this field.

Machine learning is a multi-disciplinary subject that has emerged in the past 20 years and involves many disciplines such as probability theory, statistics, approximation theory, convex analysis, and computational complexity theory.

C + + computer vision ccv-based on C language/provides cache/core machine Vision Library, novel Machine Vision Library opencv-It provides C + +, C, Python, Java and MATLAB interfaces, and supports Windows, Linux, Android and Mac OS operating system. General machine learning Mlpack dlib Ecogg Shark Closure Universal machine learning Closure Toolbox-cloj ...

We compare deep learning with machine learning and discuss their differences in all aspects. In addition to the comparison of deep learning and machine learning, we will also study their future trends.

Learning methods depending on the type of data, there are different ways to model a problem. In the field of machine learning or artificial intelligence, people first consider the way of learning algorithms. In the field of machine learning, there are several main ways of learning. 　　It is a good idea to classify the algorithm according to the learning style, so that people can choose the most suitable algorithm according to the input data to get the best results when modeling and algorithm selection. Supervised learning: Under supervised learning, input data is called "training data", each group training number ...

Machine learning uses algorithms to extract information from raw data and present it in some type of model. We use this model to infer other data that has not been modeled.

Algorithms in Machine Learning (1) - Random Forest and GBDT Based on Decision Tree Model Combination. Decision Tree This algorithm has many good features, such as training time complexity is low, the prediction process is relatively fast, the model is easy to display (easy to get the decision tree made of pictures) and so on. But at the same time, the single decision tree has some bad points, such as easy over-fitting, although there are some ways, such as pruning can reduce this situation, but not enough. Model combinations (say Boosting, Bagging, etc.) are related to decision trees ...

Machine Learning (ML) studies these patterns and encodes human decision processes into algorithms. These algorithms can be applied to several instances to arrive at meaningful conclusions.