Shulou(Shulou.com)06/01 Report--

This article mainly explains "what needs to be prepared for zero-based Python". The explanation in this article is simple and clear, easy to learn and understand. Please follow the ideas of Xiaobian and go deep into it slowly to study and learn "what needs to be prepared for zero-based Python" together!

Stage 1: Professional Core Foundation

Phase objectives:

1. Proficient in Python development environment and core programming knowledge

2. Proficient in Python object-oriented knowledge for program development

3. Deep understanding of Python core libraries and components

4. Skilled application of SQL statements for common database operations

5. Proficient in Linux operating system commands and environment configuration

6. Proficient in MySQL, master advanced database operations

7. Ability to integrate knowledge to complete projects

Knowledge Points:

Python programming basics, Python object-oriented, Python advanced, MySQL database, Linux operating system.

1. Python programming basics, syntax rules, functions and parameters, data types, modules and packages, file IO, training solid Python programming fundamentals, and skilled use of Python core objects and library programming.

2. Python object-oriented, core object, exception handling, multithreading, network programming, in-depth understanding of object-oriented programming, exception handling mechanism, multithreading principle, network protocol knowledge, and skilled application in projects.

3. Principle of class, MetaClass, special method of underscore, recursion, magic method, reflection, iterator, decorator, UnitTest, Mock. In-depth understanding of object-oriented underlying principles, master Python development advanced technology, understand unit testing technology.

4, database knowledge, paradigms, MySQL configuration, commands, database table building, data addition, deletion, query, constraints, views, stored procedures, functions, triggers, transactions, cursors, PDBC, in-depth understanding of database management system general knowledge and MySQL database use and management. A solid foundation for Python background development.

Linux installation configuration, file directory operation, VI command, management, users and permissions, environment configuration, Docker, Shell programming Linux as a mainstream server operating system, is the key technology that every development engineer must master, and can skillfully use.

Phase 2: Python WEB Development

Phase objectives:

1. Proficient in Web front-end development technology, HTML, CSS, JavaScript and front-end framework

2. In-depth understanding of front-end interaction processes and communication protocols in Web systems

3. Proficient use of Web front-end and mainstream frameworks such as Django and Flask to complete Web system development

4. Deep understanding of network protocols, distributed, PDBC, AJAX, JSON, etc.

5. Be able to use the knowledge learned to develop a MiniWeb framework and master the principles of framework implementation

6. Cross-project implementation using Web development frameworks

Knowledge Points:

Web front-end programming, Web front-end advanced, Django development framework, Flask development framework, Web development project practice.

1. Web page elements, layout, CSS style, box model, JavaScript, JQuery and Bootstrap Master front-end development technology, master JQuery and Bootstrap front-end development framework, complete page layout and beautification.

2, front-end development framework Vue, JSON data, network communication protocol, Web server and front-end interaction skillfully use Vue framework, in-depth understanding of HTTP network protocol, skilled use of Swagger, AJAX technology to achieve front-end interaction.

3. Custom Web development framework, basic use of Django framework, Model attributes and backend configuration, Cookies and Session, Templates, ORM data model, Redis secondary cache, RESTful, MVC model Master common APIs of Django framework, integrate front-end technologies, and develop complete WEB systems and frameworks.

Flask installation configuration, App object initialization and configuration, view function routing, Request object, Abort function, custom error, view function return value, Flask context and request hook, template, database extension Flask-Sqlalchemy, database migration extension Flask-Migrate, mail extension Flask-Mail. Master the common APIs of Flask framework, similarities and differences with Django framework, and be able to independently develop complete WEB system development.

Stage 3: Crawlers and Data Analysis

Phase objectives:

1. Proficient in crawler operation principle and common network capture tools, able to capture HTTP and HTTPS protocol packet analysis

2. Proficient in various common web page structure parsing library to parse and extract the grab results

3. Proficient in various common anti-crawling mechanisms and coping strategies, able to deal with common anti-crawling measures

4. Proficient in using commercial crawler framework Scrapy to write large web crawlers for distributed content crawling

5. Proficient in data analysis concepts and workflow

6. Proficient in using mainstream data analysis tools Numpy, Pandas and Matplotlib

7. Proficient in data cleaning, sorting, format conversion, data analysis report writing

8. Be able to comprehensively use the crawler to crawl Douban movie comment data and complete the data analysis.

Knowledge Points:

Web crawler development, Numpy for data analysis, Pandas for data analysis.

1. Crawler page crawling principle, crawling process, page parsing tool LXML, Beautiful soup, regular expression, proxy pool writing and architecture, common anti-crawling measures and solutions, crawler framework structure, commercial crawler framework Scrapy, based on the analysis and understanding of crawler crawling principle, website data crawling process and network protocol, master the use of page parsing tools, and be able to flexibly respond to most website anti-crawling strategies. Have the ability to write crawler framework independently and skillfully apply large commercial crawler framework to write distributed crawler.

2. Characteristics of ndarray data structure in Numpy, data types supported by Numpy, array creation methods, arithmetic operators, matrix product, self-increment and self-subtraction, general functions and aggregate functions, slice index, vectorization and broadcast mechanism of ndarray, familiar with common use of Numpy, one of the three sharp tools of data analysis, familiar with characteristics and common operations of ndarray data structure, master fragmentation, index, matrix operation and other operations for ndarray arrays of different dimensions.

3, Pandas inside the three major data structures, including Dataframe, Series and Index object basic concepts and use, index object replacement and deletion index, arithmetic and data alignment methods, data cleaning and data normalization, structure conversion, familiar with the common use of Pandas, one of the three major tools of data analysis, familiar with the use of Pandas in the three major data objects, able to use Pandas to complete the most important data cleaning, format conversion and data normalization work in data analysis, Pandas file reading and operation method.

4. Matplotlib three-layer structure system, various common chart types line chart, histogram, stacking histogram, pie chart drawing, legend, text, marking line addition, visual file preservation, familiar with the common use of Matplotlib, one of the three tools of data analysis, familiar with Matplotlib three-layer structure, able to skillfully use Matplotlib to draw various common data analysis charts. Be able to comprehensively use various data analysis and visualization tools taught in the course to complete the whole process of stock market data analysis and forecasting, shared bicycle user group data analysis, global happiness index data analysis and other projects.

Stage 4: Machine Learning and Artificial Intelligence

Phase objectives:

1. Understand the basic concepts and system processing flow related to machine learning

2. Ability to skillfully apply various common machine learning models to solve supervised learning and unsupervised learning training and testing problems, solve regression and classification problems

3. Familiar with common classification algorithms and regression algorithm models, such as KNN, decision tree, random forest, K-Means, etc.

4. Master the processing methods of convolutional neural network for image recognition and natural language recognition problems, and be familiar with tensor, session and gradient optimization models in deep learning framework TF.

5. Master the operation mechanism of deep learning convolutional neural network, and be able to customize convolution layer, pooling layer and FC layer to complete image recognition, handwriting font recognition, Captcha recognition and other conventional deep learning actual combat projects

Knowledge Points:

1. Machine learning common algorithms, use of sklearn data sets, dictionary feature extraction, text feature extraction, normalization, standardization, data principal component analysis PCA, KNN algorithm, decision tree model, random forest, linear regression and logistic regression models and algorithms. Familiar with machine learning related basic concepts, proficient in machine learning basic workflow, familiar with feature engineering, able to use a variety of common machine learning algorithm models to solve classification, regression, clustering and other problems.

2. Basic concepts related to Tensorflow, TF data flow graph, session, tensor, tensorboard visualization, tensor modification, TF file reading, tensorflow playround use, neural network structure, convolution calculation, activation function calculation, pooling layer design, master the differences and exercises before machine learning and deep learning, master the basic workflow of deep learning, master the structural hierarchy and characteristics of neural network, master the use of tensor, graph structure, OP objects, etc., familiar with input layer, The design of convolution layer, pooling layer and full connection layer completes the whole process of common deep learning projects such as Captcha recognition, image recognition and handwriting input recognition.

Thank you for reading, the above is the content of "what needs to be prepared for zero-based Python". After studying this article, I believe that everyone has a deeper understanding of what needs to be prepared for zero-based Python. The specific use situation still needs to be verified by practice. Here is, Xiaobian will push more articles related to knowledge points for everyone, welcome to pay attention!

Welcome to subscribe "Shulou Technology Information " to get latest news, interesting things and hot topics in the IT industry, and controls the hottest and latest Internet news, technology news and IT industry trends.