How long does python training take?

Participate in Python training classes with zero foundation. Python full-stack development artificial intelligence course training time is generally 5 to 6 months!

The training content includes the following stages

Phase 1: Basics of Python development

The basics of Python development course content includes: computer hardware, operating system principles , Install Linux operating system, common commands for Linux operating system maintenance, Python language introduction, environment installation, basic syntax, basic data types, binary operations, process control, character encoding, file processing, data types, user authentication, three-level menu programs, Shopping cart program development, functions, built-in methods, recursion, iterators, decorators, built-in methods, employee information table development, cross-directory import of modules, common standard library learning, b encryption\re regularity\logging log modules, etc., software development Standardized learning, calculator programs, ATM program development, etc.

Phase 2: Python Advanced Programming & Database Development

The content of the Python Advanced Programming & Database Development course includes: object-oriented introduction, features, member variables, Methods, encapsulation, inheritance, polymorphism, class generation principles, MetaClass, the role of __new__, abstract classes, static methods, class methods, attribute methods, how to use object-oriented thinking in programming to write programs, course selection program development, TCP/ Introduction to IP protocol, Socket network socket module learning, simple remote command execution client development, C\S architecture FTP server development, threads, processes, queues, IO multi-channel models, database types, feature introduction, table field types, tables Structure building statements, commonly used CRUD statements, indexes, stored procedures, views, triggers, transactions, grouping, aggregation, paging, connection pools, database-based student management system development, etc.

Phase 3: Front-end development

Front-end development course content includes: HTML\CSS\JS learning, DOM operation, JSONP, native Ajax asynchronous loading, shopping mall development, Jquery, animation effects, events, scheduled periods, carousels, marquees, HTML5\CSS3 syntax learning, bootstrap, drawer hot list development, introduction to popular front-end frameworks, Vue architecture analysis, mvvm development ideas, Vue data binding and calculations Properties, conditional rendering classes and style binding, form control binding, event binding webpack usage, vue-router usage, vuex one-way data flow and application structure, vuex actions and mutations hot reloading, Vue single page project practical development, etc. .

Phase 4: WEB framework development

WEB framework development course content includes: analysis of Web framework principles, Web request life cycle, self-developed simple Web framework, MTV\ Introduction to MVC framework, use of Django framework, routing system, template engine, FBV\CBV view, Models ORM, FORM, form verification, Django session & cookie, CSRF verification, XSS, middleware, paging, custom tags, Django Admin, cache System, signal, message, custom user authentication, Memcached, redis cache learning, RabbitMQ queue learning, Celery distributed task queue learning, Flask framework, Tornado framework, Restful API, BBS Blog practical project development, etc.

Stage Five: Crawler Development

Crawler development course content includes: Requests module, BeautifulSoup, Selenium module, PhantomJS module learning, login based on requests: drawer, github, Zhihu, blog garden, crawling hook job information, developing Web version of WeChat, high-performance IO performance related modules: asyncio, aiohttp, grequests, Twisted, custom development of an asynchronous non-blocking module, verification code image recognition, Scrapy framework and source code Analysis, introduction to framework components (engine, spider, downloader, scheduler, pipeline), distributed crawler practice, etc.

Stage 6: Full-stack project practice

Full-stack project practice course content includes: explanation of the professional development process of Internet enterprises, explanation of git, github collaborative development tools, and task management System explanation, interface unit testing, introduction to agile development and continuous integration, django uwsgi nginx production environment deployment learning, interface document writing examples, in-depth explanation of large-scale project architecture diagrams for Internet companies, CRM customer relationship management system development, etc.

Stage 7: Data Analysis

Data analysis course content includes: basic concepts of finance and stock knowledge, introduction to common investment tools, basic market trading rules, A shares Composition, etc., analysis of various technical indicators such as K-line, average line, KDJ, MACD, etc., stock market operation simulation plate demonstration of the development process of quantitative strategies, financial quantification and Python, numpy, pandas, matplotlib module common functions learning online quantitative investment platform: You Introduction and use of Mine, Jukuan, Mikuang, etc., and learning of common quantitative strategies, such as double moving average strategy, factor stock selection strategy, factor stock selection strategy, small market capitalization strategy, turtle trading rule, mean reversion, strategy, momentum strategy, reversal Strategies, Yangtuo trading rules, PEG strategies, etc., develop a simple quantitative strategy platform to realize functions such as stock selection, timing, position management, stop-profit and stop-loss, backtest result display, etc.

Stage 8: Artificial Intelligence

Artificial intelligence course content includes: machine learning elements, common genres, natural language recognition, analysis principle word vector model word2vec, analytical classification, clustering, decision tree, random forest, regression and neural network, test set and evaluation standard Python Common machine learning libraries scikit-learn, data preprocessing, Tensorflow learning, CNN and RNN models based on Tensorflow, production of two common data sources in Caffe, detailed explanation of OpenCV library, face recognition technology, automatic license plate extraction and masking, drone development , Keras deep learning, Bayesian model, driverless simulator use and development, Tesla remote control API and automated driving development, etc.

Phase 9: Automated Operation & Maintenance & Development

The content of the Automated Operation & Maintenance & Development course includes: designing a CMDB asset management system that meets the actual needs of the enterprise, such as secure API interfaces Development and use, development of clients that support Windows and Linux platforms, open and flexible APIs for other systems, design and development of IT asset online, offline, change processes and other business processes. IT audit Host management system development, user behavior, management permissions, batch file operations, user login reports, etc. of real enterprise systems. The development of a distributed host monitoring system monitors multiple services, multiple devices, and alarm mechanisms. It is developed based on the http restful architecture to achieve horizontal expansion and can easily implement distributed monitoring and other functions.

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