1) Paste Python environment and pip list screenshots, look at everyone's readiness. Please will not have the conditions for the development of the reasons and plans.
2) Paste video study notes, requires real, not plagiarism, handwriting can take pictures.
3) What is machine learning, what classification? With case, write your understanding.
Machine learning is more than one field of cross-disciplinary, involving probability theory, statistics, approximation theory , convex analysis, algorithmic complexity theory and other subjects. How specializing in computer simulation or realization of human learning behavior to acquire new knowledge or skills, re-organize existing knowledge structures so as to continuously improve their performance.
It is artificial intelligence core , is the fundamental way a computer with a smart, throughout all areas of application of artificial intelligence, it is mainly the use of induction, rather than a comprehensive interpretation.
Into the mainstream of the current machine learning: supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning.
Supervised learning the training data is labeled to infer from a functional machine learning tasks. In supervised learning, each instance is composed of an input object (typically a vector) and a desired output value (also referred to as a supervisory signal) components. Supervised learning algorithm is to analyze the training data, and generate a putative function, which can be used to map out the new instance. An optimal solution will allow the algorithm to determine the correct class label unseen examples.
Unsupervised learning - we had some problems, but do not know the answer, we need to do unsupervised learning is according to their nature they are automatically put into many groups, each group of questions is of a similar nature (such as mathematical problem will gather in one group, the English issue will be gathered in a group, physical ........ ) such as: parade
All data is not only a feature vector label, but can be found in the data showing a structure of clusters, it is of a similar nature types will be gathered together. These data without a tag into a composition, is clusters ( Clustering )
Semi-supervised learning combined with a large number of small amounts of unlabeled data and tag data in the training phase. Compared with the model uses all tag data, using a training set of training models during training can be more accurate, and lower training costs. In the real task, unlabeled samples and more, there is a small sample mark parity universal phenomenon, how to make good use of unlabeled samples to improve the model generalization, it is the focus of study and research of semi-supervised. To utilize unlabeled samples, for an assumed unlabeled samples disclosed data distribution information and labels were present contact.
Reinforcement learning is learning intelligent system mapping from the environment to conduct, so reward signal ( enhanced signal ) function value the most. Such as: walking, playing football.