1, the basic configuration before learning
I use the Python Anaconda, because it comes with a lot of packages, the basic will no longer have to use their own to install
IDE using Pycharm, particularly pip list as shown below
2, study notes
Introduction (1) Machine Learning
Machine Learning general process:
Machine learning basic math:
(2) Pyth ON foundation
3, machine learning, classification and understanding
Machine learning is usually divided into three categories
Supervised learning
Semi-supervised learning
Unsupervised Learning
Supervised learning
The characterization tag and the machine learning link between the two, no data are given in a separate feature tag, the tag can be judged. On a similar topic to see people brushing their answers, it would consolidate the knowledge, you can give the correct answer to the question.
Unsupervised Learning
Gives a lot of data but do not know the relationship between the respective characteristic data, the data needs to be in accordance with the relationship between clusters or in certain models. Like when the kids know something, see more things for a long time you will know what is a chair, table or something.
Semi-supervised learning
Semi-supervised learning a small amount of labeled data and a large number of unlabeled data, consistent and realistic comparison of data, we need to make good use of tagged data to improve the model generalization. And how to make good, it is a semi-supervised learning focus.
In addition, there are many categories such as machine learning: reinforcement learning, batch learning and online learning, and so on.