My Python full-stack development self-study notes

Although learning Python may not directly bring good job opportunities, if you want to quickly improve your ability and work efficiency at work, then it is recommended that you must learn Python.

I am a Python enthusiast. In the past seven years, I have compiled the knowledge I have acquired in learning and practical application here, so that you can check and subscribe at any time.

Don't talk nonsense and go straight to the dry goods. I hope my article can help you solve your own problems.

Python programming basics

The basics of Python programming refer to learning and mastering the basic functions and features of the Python programming language, as well as the necessary knowledge and skills for software development using Python. It includes basic syntax, data types, operators, flow control statements, functions, modules, input and output, exception handling, object-oriented programming, etc. of the Python language. Learning the basics of Python programming can help software developers and other computer professionals master basic Python programming capabilities and provide a solid foundation for further learning and practicing Python programming.

9 root reasons why Python is so popular and 4 future development directions
Python beginners' nanny-level guidance on installing Python on different systems
Python IDE The correct posture and update method
for installing PyCharm
11 lifelong skills for learning Python programming
5 data types and 8 built-in functions that must be mastered in Python basic operations 6
variable operations and 33 reserved keywords that must be mastered in
Python basics Operators and expressions that must be mastered in Python
Manipulation of string and character data that must be mastered in
Python Basics of Python must be mastered Utilize Booleans to optimize code clever methods Use
of lists List and tuples must be mastered in
Python Basics of Python Dictionaries
must be mastered Set uses
the grammatical structure that must be mastered at the basics
of Python Detailed explanation of the conditional statements that must be mastered at the basics of Python Detailed explanation of the
while infinite iterative loop method that must be mastered at the basics of Python Output methodPrint () application details that must be mastered by Python basicsDetailed explanations of string formatting operations that must be mastered by Python basicsDetailed explanations of Python function methods that must be mastered by Python basicsReturn statement usage and practice that must be mastered by Python basics






A detailed explanation of the usage of regular expressions and function flags that must be mastered in the basics of PythonDetailed explanation of the
9 types of metacharacters supported by the re module Detailed explanation of the Traceback backtracking mechanism operation that must be masteredPython foundation must be masteredPip management dependenciesPython and DVC for git version controlDetailed explanation

Python Data Processing Fundamentals

Python is a high-level, interpreted programming language that is very powerful in data manipulation. It has a wealth of built-in libraries and third-party libraries, which can easily perform operations such as data reading, cleaning, transformation, analysis, and visualization. It is also a very suitable programming language for data processing, with broad application prospects and great development potential.

Python's data structures include lists, tuples, dictionaries, and sets, which can store various types of data. It also supports regular expressions, string operations and mathematical operations, etc., which can quickly process string and numeric data.

Python also provides third-party libraries such as NumPy, Pandas, and SciPy to easily perform advanced data processing tasks such as scientific computing, statistical analysis, and machine learning.

Data Science Essentials Python Programming Basics
Data Science Essential Tools Jupyter Operation Introduction Detailed
Data Science Essential Python Using Pandas to Read and Write Data Query
Data Science Essential Python Using Pandas to Process DataFrame Data
Data Science Essential Pandas Methods of Reading and Writing CSV Files Detailed explanation
of the 6 types of file reading and writing methods in Pandas for data scienceIntroduction to
data sciencePython for data scienceProcessing Index data with PandasPython
for data sciencePython for
data sciencePython for data sciencePandas data splicing skillsSummaryPython
for data science Use Pandas data reshaping and pivot tablesPandas
data groupingGroupBy method for
data scienceRequired for data sciencePandas data sorting methodSummary for data
sciencePythonUsing Pandas for data visualizationRequired for data sciencePandas data analysis
and visualizationCommon examples
for data scienceRequired for data science Prepare Python, use Pandas data processing skills,
data science must-have Pandas data preprocessing methods summary
data science must-have Pandas data processing acceleration skills summary data science
must-have Pandas dataset operation detailed explanation data science must-have
Python use Numpy method to summarize
data science must-have Python Use Pandas method to summarize
data science essentials Pandas and NumPy for data cleaning method detailed explanation
data science essential use Python for descriptive statistics data analysis detailed explanation
Unpopular but very practical skills of Pandas necessary for data science

Python Data Manipulation Tips

Python has a rich set of built-in functions and third-party libraries that support a variety of data manipulation techniques. In Python, data types (such as lists, tuples, dictionaries) and operators (such as indexing, slicing, and comprehensions) from the standard library can be used to manipulate data. It is a powerful programming language that can support various data processing techniques, and through the support of third-party libraries, it can complete complex data processing tasks more efficiently.

In addition, Python also provides many third-party libraries, such as NumPy, Pandas, etc., which can process various data more efficiently. These libraries provide a large number of array operation functions, matrix operation functions, statistical functions, etc., which can help developers quickly complete complex data processing tasks.

In Python, you can also use various popular data visualization libraries, such as Matplotlib, Seaborn, etc., to help developers visualize data. These libraries provide a large number of chart types and rich customization options, which can help developers create a variety of beautiful data visualization charts.

Python data processing Tips Date and time data processing method summaryPython
data processing tipsData preprocessing operation method summaryPython
data processing tipsCorrectly handle data conflicts and sample selection methodsPython
data processing tips5 common processing methods and visualization of data outliers
Python data processing Tips 8 common processing methods and visualization of data missing valuesPython
data processing Tips data feature processing standardization and normalizationPython
data processing tips data feature processing discretization and binarizationPython
data processing tips data sample imbalance solution
Python data processing Tips 5 commonly used processing methods for discrete variables Python
data processing Tips 10 commonly used processing methods for continuous
variables Processing Tips Machine Learning English Dataset 8 Algorithms Applying Python Data Processing Tips Machine Learning Chinese Data 8 Common Processing Methods Python Data Processing Tips Multi-layer Nested Json Data Flattened to DataFrame Data Science Essentials Json Serialized Data for Reading and Writing Methods Detailed explanation of the three ways of image storage and access in Python Detailed explanation of the knowledge of processing data with PySpark in Hadoop cluster Detailed explanation of data science essentials Generation of random data and security in Python Detailed method









Summary of methods to solve the problem that Python cannot write to Mongodb data warehouse

Python web crawler basics

A Python web crawler is a program written in the Python language that can automatically visit a website and extract the required information. It is commonly used for tasks such as collecting large amounts of website data, monitoring website changes, analyzing website content, and testing web pages. It is a powerful tool that can automatically collect and analyze website data and has a wide range of applications in data science, market research, and information search.

The basic knowledge of Python web crawlers includes front-end technologies such as network protocols, HTTP protocols, HTML, CSS, and JavaScript, as well as common frameworks and libraries for web crawlers, such as Requests, BeautifulSoup, and Selenium.

When writing a Python web crawler, you need to pay attention to complying with the web crawler protocol, respect the website's robot protocol, and also consider anti-crawler measures, such as IP restrictions, verification codes, and cookies.

What is a web crawler? How it works and how to automatically crawl
the 2022 Internet crawler framework TOP10 analysisPython
crawler entry basic principlePython
Three Kingdoms character data quick collection minimalist method summaryWeb
crawler data storageMySQL and Mongodb method summaryPython
crawling dynamic sequences in web pages Summary of various methods of using Python
to simulate page login
Fiddler Web crawling Wechat public account history All articles
Centos7 . Change the relative path to an absolute path with one click. The web crawler can’t locate the iframe data frame in the page. I thought that selenium was omnipotent, but after crawling Youzhi, I found that nothing works.

Python Scrapy crawler framework

Scrapy is a powerful web crawler framework developed based on Python, which can quickly build an efficient and scalable web crawler system.

Scrapy uses the design idea of ​​a distributed system, supports multiple schedulers, downloaders, pipelines and spiders, and can flexibly combine various modules to complete different crawling tasks.

Scrapy provides a series of advanced features, such as automatic tracking links, simulated login, automatic processing of cookies and verification codes, use of proxy IP, etc., which can deal with complex website anti-crawling measures.

Scrapy also supports multiple data output formats, such as JSON, XML, CSV and SQLite, etc., and can export the crawled data to different storage media.

Scrapy 2.6 Architecture overview
Scrapy 2.6 Command line tool Command line tool usage guide
Scrapy 2.6 Spiders crawler script usage guide
Scrapy 2.6 Selectors data selector usage guide
Scrapy 2.6 Items data item definition, loading, transmission usage guide
Scrapy 2.6 Downloader Middleware downloader middle Scrapy
2.6 Spider Middleware Scrapy
2.6 Exceptions Exception
Handling Scrapy 2.6 Pipeline Data Transmission Pipeline
Scrapy 2.6 Cookies Local Terminal Data Settings Scrapy
2.6 Link Extractors Link Extractors Scrapy
2.6 Feed exports Data file output usage guide
Scrapy 2.6 Settings framework configuration usage guide
Scrapy 2.6 Requests and Responses request and response usage guide

Python Scrapy crawler project

The Python Scrapy crawler project is a web crawler application developed using the Scrapy framework, which can automatically visit websites and extract interesting information. It is an application that can automatically collect and analyze website data, and has a wide range of applications in data science, market research, and information search.

Use Django to deploy and manage distributed deployment in different development environmentsScrapy crawler scriptPublic
opinion monitoring data collectionScrapy environment construction and data management solutionsPublic opinion
monitoring data collectionScrapy target collation and data preparationPublic
opinion monitoring data collectionScrapy standardized crawler data captureGeneral code writing template

Python Django web framework

Django is a Python-based web development framework designed to quickly build secure, scalable, and efficient web applications. It is a powerful and easy-to-use web development framework, which is suitable for various types of web application development and is widely welcomed by Python developers.

Django provides a rich set of functional modules, such as ORM, template engine, form validation, authentication system, cache, middleware, etc., which can solve common web development tasks.

Django adopts the MVC design pattern to separate business logic, data model and presentation, making the code easier to maintain and expand. It also provides a powerful URL routing system, which can easily assign URLs to various functions of the application.

Django supports a variety of database systems, including MySQL, PostgreSQL, SQLite, etc., which can facilitate database access and operation.

Django 4.x Overviewses Framework Overview
Django 4.x Models App settings Model Application Settings
Django 4.x Models Meta Metadata Options
Django 4.x Models Field Field Options & Types
Django 4.x Models Relationship fields Field Association Relationships
Django 4.x Models CRUD (add, delete, modify, check) function method
Django 4.x CSRF site protection example and usage configuration method
Django 4.x Templates rendering page template usage example and configuration method
Django 4.x Session session usage example and configuration method
Django 4.x Caches Cache usage example and configuration method
Django 4.x File file upload usage example and configuration method
Django 4.x Static static file usage example and configuration method
Django 4.x Email mail usage example and configuration method
Django 4.x DataBases Data Warehouse Usage Examples and Configuration Methods
Django 4.x Auth Authentication Usage Examples and Configuration Methods
Django 4.x Test Unit Test Usage Examples and Configuration Methods
Django 4.x Globalization Globalization Usage Examples and Configuration Methods
Django 4.x Logging Log Usage Examples and configuration methods
Django 4.x Message message usage examples and configuration methods
Django 4.x HTTP network protocol usage example and configuration method
Django 4.x WWW URL usage example and configuration method
Django 4.x quickly configure a Django Web project setting template

Python DRF API framework

Django REST framework (DRF for short) is a powerful Web API development toolkit developed based on the Django framework, which can quickly build RESTful APIs. It is an excellent Web API development toolkit, which can quickly build high-quality RESTful APIs, and has a wide range of applications in microservices, mobile applications, and Web applications.

DRF provides a series of tools for processing HTTP requests and responses, including views, serializers, filters, permission control, etc., which can be easily integrated with Django's models and views.

DRF supports data exchange in multiple formats, including JSON, XML, YAML, etc., and can be easily integrated with various client applications. It also supports the hypermedia API framework, which can make the API more flexible and extensible.

DRF also provides a series of tools for testing and debugging, such as automatically generating API documents, visualizing API interfaces and simulating client requests, etc., which can easily manage and maintain APIs.

DRF 3.x Overviews Framework Overview
DRF 3.x Authentication Identity Authentication Usage Example and Configuration Method
DRF 3.x Caching Caching Usage Example and Configuration Method
DRF 3.x Content Negotiation Content Negotiation Usage Example and Configuration Method
DRF 3.x Filtering Filtering Usage Example and configuration method
DRF 3.x Format Suffixes format suffix usage example and configuration method
DRF 3.x Parsers parser usage example and configuration method
DRF 3.x Permissions permission usage example and configuration method
DRF 3.x Renderers renderer usage example and Configuration Method
DRF 3.x Request and Response Request and Response Example and Configuration Method
DRF 3.x URLs and Router Link and Routing Example and Configuration Method
DRF 3.x Serializers Serialization Usage Example and Configuration Method
DRF 3.x APP Model Serializer application model serialization usage example and configuration method
DRF 3.x Status Codes status response usage example and configuration method
DRF 3.x Throttling throttling usage example and configuration method
DRF 3.x Validators verification usage example and configuration method
DRF 3.x View and API view and API application usage examples and configuration methods

Python Django feature development

Python Django web function development refers to the use of Django framework to develop the functions of web applications, including business logic, data model, presentation layer, etc. It is a systematic work that requires the comprehensive use of various technologies and tools to realize the business logic and presentation layer of Web applications.

Django provides a wealth of tools and techniques to develop functions, such as model layer, view layer, template layer, middleware, etc. The model layer is used to define the data model, including fields, relationships, constraints, etc. The view layer is used to implement business logic, handle HTTP requests and responses, and call the model layer for data manipulation. The template layer is used to define the presentation layer, using HTML, CSS, JavaScript and other technologies to present data.

Middleware are optional plugins that provide additional functionality between the model, view, and template layers, such as authentication systems, caching, access control, and more.

Centos7.x builds MySQL 5.7 and error handling
Centos7.x builds MongoDB 3.4 and user settings
Centos7.x solves the problem of using sqlite3
Centos7.x quickly configures Django+Nginx+Uwsgi service
Django implementation adminx background statistics foreign key associated content data
Django implementation adminx background website access IP record statistics method
Django implements CMS content management system content access cumulative sum
Django implements API and JWT for user authentication method
Django uses JavaScript to implement JWT user login and logout
Django rewrites User model and modifies plaintext password encryption method
Django implements CMS content management system mptt comment function
Django cloud server configuration Nginx site SSL certificate HTTPS protocol
Django implements adminx edit details page hides specified field method
Django implements adminx edit details page automatically fills current userDjango
implements adminx update form automatically synchronizes to other
modelsDjango Realize the linkage of the first and second-level lists of the adminx edit details page
Django Realize the content editing and display of user identity identification in the adminx
background The editing interface Django realizes the function of timing tasks and sending emails (including various attachments)



Django implements adminx backstage and closes the top search bar Django implements adminx navigation menu Uses Awesome
icons
Django implements Linux server to quickly clear cache

Python Django Web Development

Django is a powerful Python web framework designed for rapid development and deployment of high-performance web applications. Django has rich functions and components, and can quickly build various web applications, including blogs, forums, shopping malls, etc. It is a very suitable framework for web development, which provides rich functions and components, and can quickly build and deploy high-performance web applications.

In Django, you can use the views (Views) and templates (Templates) to process user requests and responses, use models (Models) to manage data, use forms (Forms) to process user input, and use tests (Tests) to Ensure the quality of the code.

Django also provides many built-in components, such as authentication (Authentication), authority management (Permissions), site management (Sites), etc., which can quickly build a complete web application.

Python news website project-1. Project analysis and product designPython
news website project-2.Scrapy crawler configuration and data capturePython
news website project-3.Gerapy crawler distributed deploymentPython
news website project-4.Data processing and algorithms Application of
Python news website project-5.Django news website front-end display
Python news website project-6.Django content background management system configurationPython
news website project-7.Django content background management system management developmentPython
news website project-8.Django front-end Web DeploymentPython
News Website Project-9.Django Front-End HTML FunctionPython
Personal Homepage Project-1.Project Analysis and Product DesignPython
Personal Homepage Project-2.Project Creation and Environment ConfigurationPython
Personal Homepage Project-3.Personal Data Backend Management Application Develop
Python personal homepage project-4. Front-end rendering templatePython
website navigation project-1. Project analysis and product designPython
website navigation project-2. Project creation and environment configurationPython
website navigation project-3.Navigation data back-end management application
developmentPython Website navigation project-4. Front-end rendering templatePython
website navigation project-5. Batch grabbing logo icons of navigation websites
WebStackHugo navigation open source project deployment process and content One-click configuration

Data Analyst Fundamentals

Data analysts are professionals responsible for analyzing and mining data, so they need to master the basic knowledge and methods of statistics and mathematics. These knowledge and methods can help data analysts to perform descriptive statistics, regression analysis, classification and cluster analysis on data, and can provide data analysts with effective tools and methods to analyze, mine, visualize and summarize data.

However, data analysts do not need to study statistics and mathematics in depth, because in their daily work, they usually only need to master basic statistical and mathematical methods. For more in-depth knowledge of statistics and mathematics, data analysts can study selectively as needed to meet specific analysis needs.

It doesn’t matter if you don’t know
Python. Mastering the 36 skills in Excel is the same as doing data analysis
. Data analysts must be able to write PPT in the end. 5 principles
for data analysis, what is statistics? for
data analysis, what is data & data analysis?
for data analysis, what is summary measure? for data analysis, what is dispersion
measure
For
data analysis, you need to know what is numerical data. For data
analysis, you need to know what categorical data is.
For data analysis, you need to know what standard scoring & dispersion is. For data
analysis, you need to know what statistics & sampling distribution are
for data analysis. To know what is bivariate correlation analysis,
to enter data analysis, to know what is independence test & goodness of fit test, to
enter data analysis, to know what is normal distribution and other variable distribution,
to enter data analysis, to know what is univariate analysis and Basic statistics
to know the multivariate analysis and common methods of practical application
in data analysis To start data analysis, you need to know what
is data mining and introductory application methods

Data Analysis Data Warehouse

Data Analysis A data warehouse is a software system for storing and managing data, primarily to help data analysts or other data professionals acquire and analyze data quickly. It is a very useful software system that can help data analysts or other data professionals quickly acquire and analyze data and improve work efficiency.

Such systems usually provide a data model and a set of data management tools, allowing developers to store and manage data conveniently, and support multiple data sources, such as databases, files, networks, and so on.

The data analysis data warehouse also supports data extraction, transformation, and loading (ETL) operations, so that developers can quickly extract data from different sources into the data warehouse, and perform operations such as cleaning, transformation, and loading on the data, making the data change. more unified and structured.

MySQL 5.7 Installation and Uninstallation
Introduction to MySQL Data Warehouse
MySQL Data Warehouse Basics
MySQL Table Structure Management
MySQL Table Update Record
MySQL Table Record Retrieval
MySQL Programming Basics
MySQL Views & Triggers

Python Pyecharts data visualization

Pyecharts is a Python-based data visualization tool that provides a large number of beautiful chart types and rich customization options. It is developed based on Echarts, a popular data visualization library that supports various statistical charts, maps, scientific charts, timelines, etc. It is a powerful Python data visualization tool that supports various beautiful chart types and rich customization options, helping developers quickly create various beautiful data visualization charts. Its usage is easy to learn, and charts can be easily embedded into web pages or reports, and it supports various data sources, allowing developers to easily present their own data.

Pyecharts can be easily used in Python and provides a Pythonic interface, allowing developers to use Python syntax to create various charts. It also supports saving charts as files in various formats, such as HTML, pictures, etc., which is convenient for embedding charts into web pages or reports.

Pyecharts supports the use of various data sources, such as lists, tuples, dictionaries, Pandas, etc., and provides rich chart types, such as bar charts, line charts, pie charts, scatter plots, maps, etc. It also supports various customization options to adjust the style, layout, theme, etc. of the chart.

Python data visualization Pyecharts global configuration
Python data visualization Pyecharts series configuration
Python data visualization Pyecharts set the main style Python data visualization Pyecharts
configuration Faker data set
Python data visualization Pyecharts chart rendering
Python data visualization Pyecharts make Bar columnar, bar graph
Python data visualization Pyecharts production Bar3D 3D histogram
Python data visualization Pyecharts Make Boxplot box plot
Python data visualization Pyecharts Make Calendar calendar heat map
Python data visualization Pyecharts Make Effectscatter ripple scatter
plot Python data visualization Pyecharts make Funnel funnel graph
Python data visualization Pyecharts make GEO geographic coordinate system
Python Data VisualizationPyecharts Make Graph Relationship DiagramPython
Data VisualizationPyecharts Make Grid Combination ComponentPython
Data VisualizationPyecharts Make Heatmap Heat MapPython Data VisualizationPyecharts
Make Line Polyline, Area ChartPython
Data VisualizationPyecharts Make Line3D 3D Line ChartPython
Data VisualizationPyecharts Make Liquid Water Polo Diagram
Python Data VisualizationPyecharts Make Map MapPython
Data VisualizationPyecharts Make Map3D 3D MapPython Data VisualizationPyecharts
Make Overlap Cascade Multi-GraphPython Data VisualizationPyecharts Make
Page Sequential Multi-GraphPython Data VisualizationPyecharts
Make Parallel Parallel Coordinate SystemPython
Data VisualizationPyecharts Make PictorialBar Pictorial Bar FigurePython
data visualizationPyecharts make Pie pie chartPython
data visualizationPyecharts make Polar polar coordinate systemPython
data visualizationPyecharts make Radar radar chartPython data visualizationPyecharts make
Sankey Sankey graphPython
data visualizationPyecharts make Scatter scatterplotPython
data visualizationPyecharts production Scatter3D 3D scatter plot
Python data visualization Pyecharts make Sunburst sunburst graph
Python data visualization Pyecharts make Surface3D 3D surface graph
Python data visualization Pyecharts make Tab tab multi-graph control
Python data visualization Pyecharts make ThemeRiver theme river graph
Python data visualization Pyecharts make Timeline timeline components
Python Data VisualizationPyecharts Make Tree Tree MapPython
Data VisualizationPyecharts Make Treemap Rectangular Tree MapPython
Data VisualizationPyecharts Make WordCloud Word CloudPython
Data VisualizationPyecharts Make Gauge Dashboard

Python video editing framework

The Python Video Editing Framework is a software tool used to help developers quickly build video editing applications. This framework usually provides a rich set of APIs and tools, allowing developers to easily edit, merge, adjust volume, add subtitles, and add watermarks to videos. It is a very useful tool that can help developers quickly build feature-rich and efficient video editing applications.

In addition, the Python video editing framework also supports video format conversion, video encoding, video decoding and other operations, allowing developers to convert videos into various formats for easy playback on various devices and platforms.

The basics of Python video production MoviePy framework using
Python video production MoviePy framework video processing module
Python video production MoviePy framework graphic processing module
Python video production MoviePy framework audio processing module
Python video production MoviePy framework video afx audio effect example
Python video production MoviePy framework video vfx Example of video effects
Use FFmpeg to merge multiple audios and solve the sound reduction method
Use FFmpeg to swap horizontal and vertical versions of video Background blur One-click generation of
Python video clips Auto-Editor One-click preprocessing Mouth broadcast silent clips
Python video clips AutoCut Synthesis in documents Make Oral Videos

Python video editing project

A Python video editing project usually consists of a set of Python code and video resources, and is developed using the Python video editing framework. It is a video editing application developed using Python language and related frameworks, which can help developers quickly build feature-rich and efficient video editing applications.

Developers can edit, merge, adjust volume, add subtitles, and add watermarks to videos by writing Python codes.

In addition, the Python video editing project can also perform video format conversion, video encoding, video decoding and other operations through the Python video editing framework, so that the video can be played on various devices and platforms.

The first edition of the whole network's first Python Douyin blinds short video card point automatic mixing and
cutting The first edition of the Internet's first automatic editing of pictures and texts in Python for popular science in traditional Chinese medicine No need to change clothes. A video editor imitates Toutiao's original article to generate a video with one click, and it can finally be released on all platforms.

Python utility applications

Python utility applications are practical applications developed using the Python language. It is a practical application program developed in Python language, which can help users solve various practical problems and provide a convenient user experience.

Such applications are usually developed for a specific task or function, and can bring practical help and convenience to users.

The range of practical applications for Python is vast and can cover areas as diverse as computer graphics, computer networking, data analysis, machine learning, video editing, game development, and more.

In addition, the Python utility application also has the advantage of being cross-platform, and can run on various operating systems such as Windows, Mac, and Linux.

Python builds a QQ chat robot minimalist tutorial
OpenAI's strongest dialogue model ChatGPT registration notes

Python Machine Learning Algorithms

Python has become the programming language of choice in the field of machine learning. Machine learning is a branch of artificial intelligence, which allows computer systems to automatically learn and acquire knowledge from data through the analysis and calculation of large amounts of data. It can be used in many different fields such as natural language processing, computer vision, predictive analytics, etc.

Algorithms used in machine learning are those that are capable of automatically learning and extracting knowledge from data. These algorithms are generally divided into four categories: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Among them, the supervised learning algorithm needs to give the correct result of the data in advance, so as to learn how to predict the result of the new data; the unsupervised learning algorithm does not need to give the correct result of the data in advance, but clusters and analyzes the data. The relationship between learning data; semi-supervised learning algorithm is a compromise between supervised learning and unsupervised learning, it requires a small amount of labeled data and a large amount of unlabeled data; reinforcement learning algorithm is through continuous trial and learning Algorithms to optimize decision-making.

In Python, you can use many third-party libraries to implement different machine learning algorithms, such as scikit-learn, TensorFlow, PyTorch, etc. These libraries provide a large number of algorithm implementations, and provide rich documentation and sample codes for developers to use.

Understanding Accuracy Indicators and Evaluation of Regression Models with Zero Basics in Python Machine Learning with
Zero Basics in Python Understanding Accuracy Indicators and Evaluation in Classification Models with
Zero Basics in Python Machine Learning with Zero Basics in
Python Understanding Linear Regression Analysis with
Zero Basics in Python Machine Learning Basic understanding of decision tree analysisPython
machine learning zero-basic understanding of random forest algorithmPython
machine learning zero-basic understanding of K nearest neighbor algorithmPython machine learning zero- basic understanding
of support vector machines Machine Learning Zero-Basic Understanding of K-means Clustering Python Machine Learning Zero-Basic Understanding of DBSCAN Clustering Python Machine Learning Zero-Basic Understanding of PCA Principal Component Analysis Python Machine Learning Zero-Basic Understanding of Time Series ARIMA Data Science Necessary Correlation Analysis Three Operations and Detailed Visualization

Python machine learning case

Python is a popular programming language, one of the important application areas is machine learning. In machine learning, by continuously training the data, the computer can automatically learn and extract the laws in the data, so as to accurately predict or classify the unknown data. In addition to scikit-learn, Python has many other machine learning libraries, such as TensorFlow, PyTorch, etc., which can meet different machine learning needs.

In Python, you can use the famous machine learning library scikit-learn for machine learning. This library provides numerous machine learning algorithms and tools to quickly build machine learning models.

Use Python to sort out user indicators and order tracking through operational skill mapsUse
machine learning for customer lifetime value and RFM model analysisUser
lifetime valueUse XGBoost for LTV predictionUse
multiple machine learning methods for customer pre-churn or early warningUse
machine learning to predict customers Which day is the next purchase day
? Use LSTM model to predict product sales.
Discover potential users. Use XGBoost to predict product marketing response.

Python deep learning framework

Deep learning is a type of machine learning, which uses the structure of neural networks for automatic feature learning and supervised learning, and can automatically learn complex patterns in large amounts of data.

In Python, deep learning can be implemented using many third-party libraries, the most popular of which are TensorFlow, PyTorch, Keras, etc. These libraries provide a large number of neural network layers, activation functions, loss functions, optimizers, etc., allowing developers to quickly build various deep learning models.

These frameworks also provide rich documentation and sample codes for the convenience of developers. In addition, these frameworks support GPU acceleration, making model training and inference more efficient.

Win10+Python3.9+GPU version pytorch environment builds the simplest process
Win10+Python3.8+GPU version tensorflow2.x environment builds the simplest process
Tensorflow-gpu1.x version and Tensorflow-gpu2.x version coexistence method

Python deep learning case

Deep learning is an important branch of machine learning. It uses a large amount of data and computer resources to solve machine learning problems by building and training complex models. The model structure of deep learning usually consists of multiple "layers", each layer is responsible for extracting different features in the data, so that the model can automatically learn and extract the laws in the data.

In Python, you can use the famous deep learning framework TensorFlow for deep learning. The framework provides a rich set of tools and libraries to quickly build, train, and evaluate deep learning models.

An image classification model can be built using TensorFlow to classify images. First, you need to prepare some labeled image data, and then use TensorFlow to build the model structure, define the loss function and optimizer. Finally, the training data is used to train the model, and the test data is used to evaluate the model to judge the generalization ability of the model.

In addition to TensorFlow, Python has many other deep learning frameworks, such as PyTorch, Keras, etc., which can meet different deep learning needs. Deep learning has shown superior performance in solving many machine learning problems, thus, it has become the preferred solution in many application domains.

In the field of computer vision, deep learning models can be used for tasks such as image classification, object detection, and semantic segmentation. In the field of natural language processing, deep learning models can be used for tasks such as text classification, speech recognition, and machine translation. In the field of bioinformatics, deep learning models can also be used for analysis and prediction of genomic data.

In short, deep learning is a very powerful machine learning technology. In Python, you can use the famous frameworks TensorFlow, PyTorch, etc. to quickly build and train deep learning models to solve various machine learning problems.

Deep Learning Hotel-ID Combat Human Trafficking (1) Project Introduction and Data PreprocessingDeep Learning
Hotel-ID Combat Human Trafficking (2) Model Training and ValidationDeep Learning
Hotel-ID Combat Human Trafficking (3) Project Introduction and Data
PreprocessingGithub Excellent Project-Use Python to automatically generate Github based on CPM textExcellent Project-Use Python to automatically generate Github Excellent Project
based on GPT2 text -Use Python to AI anchor based on Wav2Lip

NLP natural language processing

NLP natural language processing refers to a field in computer science that specializes in how to make computers understand, generate, and interact with human language. It is a technology that allows computers to understand, generate, and interact with human language, and has made important contributions to the development of artificial intelligence and computer science. NLP natural language processing technology is constantly evolving and developing, and can bring more efficient and humanized solutions to various fields.

NLP natural language processing is mainly used in the analysis, generation, and translation of natural language texts. It is a comprehensive application in many fields such as artificial intelligence, computer science, linguistics, and psychology.

The main NLP natural language processing technologies include speech recognition, language translation, text mining, question answering system, natural language generation, etc.

NLP natural language processing technology has been widely used in various fields, such as machine translation, chat robots, intelligent assistants, news summaries, social media analysis, etc.

What is text mining and how should you learn it?
The current popular Chinese word segmentation module jieba
TF-IDF principle and multi-language application
NLP Natural language processing NLTK common English function summary
Exploring the Gensim library for NLP
NLP natural language processing Gensim word vector common applications
LSA, pLSA, LDA, NMF, BERTopic, Top2Vec for Topic Modeling

job interview

When applying for a Python job interview, applicants are usually asked to provide their personal information, work experience, project experience, etc., so that employers can understand the applicant's ability and experience.

In addition, the Python interview job search will also conduct a technical interview, that is, to examine the applicant's professional knowledge and skills by asking the applicant to answer technical questions about Python.

9 cases of data engineers interviewing QA with Python