Here are some of the information I learn learning machine learning process, of course, in addition there are more than some of the articles and video, saw that a lot of the actual contents are too difficult, he is currently difficult to understand mathematical foundation to explain the contents inside . In addition, too much information but affect the efficiency of learning, a lot of content are repeated, vast amounts of books, videos and articles, and ultimately only as part of the collection, permanent sleeping in the cloud disk, only as a psychological comfort it feels like collection of this information is already later whenever you can open dry, and there is no actual use.
During this period of time to learn, I've been thinking, how can Quick Start learning machine? What steps Quick Start is it? After a period of time to see a large number of articles and learning carding, feel the need to entry is not complicated, first of machine learning have a general knowledge and understanding, to understand the basic concepts, understanding of its technology stack and do their own study plan, then find examples of machine learning algorithms call directly encoded, starting from practice, understand the algorithm model from practice.
Machine learning relevant information
The machine learning how entry
How Basics three months and "machine learning"?
ApacheCN Artificial Intelligence Knowledge Tree
To the machine learning algorithm minimalist introductory course for beginners
Machine learning introductory video tutorials
Machine Learning Edition video tutorial teaching
There are various online platforms, the machine learning-related video lessons
Mathematical basis
Mathematical foundations of machine learning - Higher Mathematics
Mathematical foundations of machine learning - Linear Algebra
Mathematical foundations of machine learning - Probability theory and mathematical statistics
Mathematical foundations of machine learning
There are various online platforms, the mathematical basis of the relevant video lessons
python tool
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NumPy is an extension library Python language, supports a number of dimensions of the array and matrix operations, in addition, it provides a lot of math library for array operations.
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Pandas is a powerful set of tools to analyze structured data
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Matplotlib is a Python 2D graphics library, which generate publication-quality graphics in various levels of hardcopy formats and interactive cross-platform environment.
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Statsmodels provide classes and functions for many different statistical models estimated, and can be explored statistical tests and statistical data.
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Seaborn matplotlib based graphical visualization package python. It provides a highly interactive interface for users to make various attractive charts.