1.python basis of preparation
The course intends to use Python as the implementation language machine algorithm, so be sure to:
1) installed Python development environment, PyCharm or so can Anaconda, according to personal habits preferences.
2) install the basic library, such as numpy, pandas, scipy, matplotlib
3) have some Python programming skills, if you are not familiar with, you can select a tutorial to learn, simple to use Python good, resources are very rich.
Python 3 rookie Tutorial Tutorial http://www.runoob.com/python3/python3-tutorial.html
Liao Xuefeng's official website A Python 3 https://www.liaoxuefeng.com/wiki/1016959663602400
Learning Videos
2. Video learning content: https://www.bilibili.com/video/BV1Tb411H7uC?p=1
1) P4 Python basis
2 P1 Introduction to Machine Learning)
Machine learning is more than one field of cross-disciplinary, involving more mathematical knowledge, we do not do too much theoretical requirements, if there do not understand the place, do not give up, go over there impressions. By watching the video, we have a general understanding of the curriculum.
Suggest that you watch and take notes, recording time point and the point where, in order to have the necessary time to look back. Learning notes also part of the job.
3. Small Exercise:
1) Paste Python environment and pip list screenshots, look readiness.
2) Paste video study notes, requires real, not plagiarism, handwriting can take pictures.
Introduction (1) P1 machine learning
Another representation, 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. Machine Learning example: unmanned vehicles.
(2) P4 Python basis
3) What is machine learning, what classification? With case, write your understanding.
What is Machine Learning:
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 the core of artificial intelligence, it is to make computers intelligent fundamental way.
Machine learning can be divided into the following several categories:
supervised learning from given training data set out a learning function, when the arrival of new data, the results can be predicted based on this function. Supervised learning the training set is required, including input and output, it can be said characteristics and objectives. The goal of the training set is marked by the people. Common supervised learning algorithms including regression analysis and statistical classification.
Supervised learning and unsupervised learning difference is whether the training set target people marked. They have the training set and has input and output
unsupervised learning and supervised learning compared to the training set without human annotation results. Common unsupervised learning algorithm generated against the network, clustering.
Semi-supervised learning ranged between supervised learning and unsupervised learning.
Enhanced learning machine in order to achieve their goals, along with changes in the environment, and gradually adjust its behavior, and evaluate after each action to the feedback is positive or negative.