(1) Paste Python environment and pip list screenshots, look at everyone's readiness. Please will not have the conditions for the development of reasons and intends to
(2) study notes affixed video requires real, not plagiarism, handwriting can take pictures.
(3) What is machine learning, what classification? With case, write your understanding.
Concept: Machine learning is a branch of artificial intelligence. We use a computer to design a system, it can in a certain way to learn the training data provided; the third training session, the system can continue to learn and improve on performance; by learning parameter optimization model can be used predictive output related issues.
classification:
Supervised Learning: In supervised learning, input data is called "training data", each set of training data have a clear identity or results, such as anti-spam system, "junk mail", "non-spam e-mail to"; the establishment predictive models when supervised learning to build a learning process, the predicted results were compared with a "training data" the actual results, continue to adjust predictive models, until the predicted results of the model to achieve a desired accuracy. Supervised learning of common scenarios such as classification and regression problems.
Unsupervised Learning: In unsupervised learning, the data are not specifically identified, learning model is to infer some of the internal structure of the data. Common scenarios include association rule learning, and clustering. Common algorithms include Apriori algorithm and k-Means algorithm.
Semi-supervised learning: In the semi-supervised learning, input data part is identified, and some have not been identified, this learning model can be used to predict, but first need to model data in order to study the internal structure of rational organizational data to predict.
Reinforcement Learning: the reinforcement learning mode, the input data as feedback to the model, unlike supervision model as input data only as a right and wrong way to check the model in reinforcement learning, input data directly back to the model, the model must In this regard make adjustments immediately.