- Python
- 1.1?基础
- while语句
- 字符串边缘填充
- 列出文件夹中的指定文件类型
- All Combinations For A List Of Objects
- Apply Operations Over Items In A List
- Applying Functions To List Items
- Arithmetic Basics
- Assignment Operators
- Basic Operations With NumPy Array
- Breaking Up String Variables
- Brute Force D20 Roll Simulator
- Cartesian Product
- Chain Together Lists
- Cleaning Text
- Compare Two Dictionaries
- Concurrent Processing
- Continue And Break Loops
- Convert HTML Characters To Strings
- Converting Strings To Datetime
- Create A New File Then Write To It
- Create A Temporary File
- Data Structure Basics
- Date And Time Basics
- Dictionary Basics
- Display JSON
- Display Scientific Notation As Floats
- Exiting A Loop
- Find The Max Value In A Dictionary
- Flatten Lists Of Lists
- For Loop
- Formatting Numbers
- Function Annotation Examples
- Function Basics
- Functions Vs. Generators
- Generating Random Numbers With NumPy
- Generator Expressions
- Hard Wrapping Text
- How To Use Default Dicts
- If Else On Any Or All Elements
- Indexing And Slicing NumPy Arrays
- Indexing And Slicing NumPy Arrays
- Iterate An Ifelse Over A List
- Iterate Over Multiple Lists Simultaneously
- Iterating Over Dictionary Keys
- Lambda Functions
- Logical Operations
- Looping Over Two Lists
- Mathematical Operations
- Mocking Functions
- Nested For Loops Using List Comprehension
- Nesting Lists
- Numpy Array Basics
- Parallel Processing
- Partial Function Applications
- Priority Queues
- Queues And Stacks
- Recursive Functions
- Scheduling Jobs In The Future
- Select Random Element From A List
- Selecting Items In A List With Filters
- Set The Color Of A Matplotlib Plot
- Sort A List Of Names By Last Name
- Sort A List Of Strings By Length
- Store API Credentials For Open Source Projects
- String Formatting
- String Indexing
- String Operations
- Swapping Variable Values
- Unpacking A Tuple
- Unpacking Function Arguments
- Use Command Line Arguments In A Function
- Using Named Tuples To Store Data
- any(), all(), max(), min(), sum()
- if and if else
- repr vs. str
- Try, Except, 和 Finally
- 1.2?数据可视化
- Back To Back Bar Plot In MatPlotLib
- Bar Plot In MatPlotLib
- Color Palettes in Seaborn
- Creating A Time Series Plot With Seaborn And pandas
- Creating Scatterplots With Seaborn
- Group Bar Plot In MatPlotLib
- Histograms In MatPlotLib
- Making A Matplotlib Scatterplot From A Pandas Dataframe
- Matplotlib, A Simple Example
- Pie Chart In MatPlotLib
- Scatterplot In MatPlotLib
- Stacked Percentage Bar Plot In MatPlotLib
- 1.3?数据整理
- 查找数据框中的唯一值
- 连接合并数据框
- Columns Shared By Two Data Frames
- Apply Functions By Group In Pandas
- Apply Operations To Groups In Pandas
- Applying Operations Over pandas Dataframes
- Assign A New Column To A Pandas DataFrame
- Break A List Into N-Sized Chunks
- Breaking Up A String Into Columns Using Regex In pandas
- Construct A Dictionary From Multiple Lists
- Convert A CSV Into Python Code To Recreate It
- Convert A Categorical Variable Into Dummy Variables
- Convert A Categorical Variable Into Dummy Variables
- Convert A String Categorical Variable To A Numeric Variable
- Convert A Variable To A Time Variable In pandas
- Count Values In Pandas Dataframe
- Create A Pipeline In Pandas
- Create A pandas Column With A For Loop
- Create Counts Of Items
- Create a Column Based on a Conditional in pandas
- Creating Lists From Dictionary Keys And Values
- Crosstabs In pandas
- Delete Duplicates In pandas
- Descriptive Statistics For pandas Dataframe
- Dropping Rows And Columns In pandas Dataframe
- Enumerate A List
- Expand Cells Containing Lists Into Their Own Variables In Pandas
- Filter pandas Dataframes
- Find Largest Value In A Dataframe Column
- Geocoding And Reverse Geocoding
- Geolocate A City And Country
- Geolocate A City Or Country
- Group A Time Series With pandas
- Group Data By Time
- Group Pandas Data By Hour Of The Day
- Grouping Rows In pandas
- Hierarchical Data In pandas
- List Unique Values In A pandas Column
- Load A JSON File Into Pandas
- Load An Excel File Into Pandas
- Load Excel Spreadsheet As pandas Dataframe
- Loading A CSV Into pandas
- Long To Wide Format
- Lower Case Column Names In Pandas Dataframe
- Make New Columns Using Functions
- Map External Values To Dataframe Values in pandas
- Missing Data In pandas Dataframes
- Moving Averages In pandas
- Normalize A Column In pandas
- Pivot Tables In pandas
- Quickly Change A Column Of Strings In Pandas
- Random Sampling Dataframe
- Ranking Rows Of Pandas Dataframes
- Regular Expression Basics
- Regular Expression By Example
- Reindexing pandas Series And Dataframes
- Rename Column Headers In pandas
- Rename Multiple pandas Dataframe Column Names
- Replacing Values In pandas
- Saving A pandas Dataframe As A CSV
- Search A pandas Column For A Value
- Select Rows When Columns Contain Certain Values
- Select Rows With A Certain Value
- Select Rows With Multiple Filters
- Selecting pandas DataFrame Rows Based On Conditions
- Simple Example Dataframes In pandas
- Sorting Rows In pandas Dataframes
- Split Lat/Long Coordinate Variables Into Seperate Variables
- Streaming Data Pipeline
- String Munging In Dataframe
- Using List Comprehensions With pandas
- Using Seaborn To Visualize A pandas Dataframe
- pandas Data Structures
- pandas Time Series Basics
- 1.4?日志
- 1.5?其它
- 1.6?测试
- 1.7?网络爬虫
- 1.1?基础
- 机器学习
- 1.1??基础知识
- Loading Features From Dictionaries
- Loading scikit-learn’s Boston Housing Dataset
- Loading scikit-learn’s Digits Dataset
- Loading scikit-learn’s Iris Dataset
- Make Simulated Data For Classification
- Make Simulated Data For Clustering
- Make Simulated Data For Regression
- Perceptron In Scikit
- Saving Machine Learning Models
- 1.2??聚类
- 1. 3?特征工程
- Dimensionality Reduction On Sparse Feature Matrix
- Dimensionality Reduction With Kernel PCA
- Dimensionality Reduction With PCA
- Feature Extraction With PCA
- Group Observations Using K-Means Clustering
- Selecting The Best Number Of Components For LDA
- Selecting The Best Number Of Components For TSVD
- Using Linear Discriminant Analysis For Dimensionality Reduction
- 1.4??特征选择
- 1.5??线性回归
- 1. 6?逻辑斯特回归
- 1. 7?模型评估
- Accuracy
- Create Baseline Classification Model
- Create Baseline Regression Model
- Cross Validation Pipeline
- Cross Validation With Parameter Tuning Using Grid Search
- Cross-Validation
- Custom Performance Metric
- F1 Score
- Generate Text Reports On Performance
- Nested Cross Validation
- Plot The Learning Curve
- Plot The Receiving Operating Characteristic Curve
- Plot The Validation Curve
- Precision
- Recall
- Split Data Into Training And Test Sets
- 1.8?模型选择
- 1.9??朴素贝叶斯
- 1.10?KNN
- 1.11?日期时间数据处理
- Break Up Dates And Times Into Multiple Features
- Calculate Difference Between Dates And Times
- Convert Strings To Dates
- Convert pandas Columns Time Zone
- Encode Days Of The Week
- Handling Missing Values In Time Series
- Handling Time Zones
- Lag A Time Feature
- Rolling Time Window
- Select Date And Time Ranges
- 1.12?图像处理
- 1.13?处理结构化数据
- Convert Pandas Categorical Data For Scikit-Learn
- Delete Observations With Missing Values
- Deleting Missing Values
- Detecting Outliers
- Discretize Features
- Encoding Ordinal Categorical Features
- Handling Imbalanced Classes With Downsampling
- Handling Imbalanced Classes With Upsampling
- Handling Outliers
- Impute Missing Values With Means
- Imputing Missing Class Labels
- Imputing Missing Class Labels Using k-Nearest Neighbors
- Normalizing Observations
- One-Hot Encode Features With Multiple Labels
- One-Hot Encode Nominal Categorical Features
- Preprocessing Categorical Features
- Preprocessing Iris Data
- Rescale A Feature
- Standardize A Feature
- 1.14?处理文本
- 1.15?支持向量机
- 1.16?树和森林
- 随机森林做特征选择
- Adaboost Classifier
- Decision Tree Classifier
- Decision Tree Regression
- Feature Importance
- Handle Imbalanced Classes In Random Forest
- Random Forest Classifier
- Random Forest Classifier Example
- Random Forest Regression
- Select Important Features In Random Forest
- Titanic Competition With Random Forest
- Visualize A Decision Tree
- 1.17?向量,矩阵和数组
- Adding And Subtracting Matrices
- Apply Operations To Elements
- Calculate Dot Product Of Two Vectors
- Calculate The Average, Variance, And Standard Deviation
- Calculate The Determinant Of A Matrix
- Calculate The Trace Of A Matrix
- Converting A Dictionary Into A Matrix
- Create A Matrix
- Create A Sparse Matrix
- Create A Vector
- Describe An Array
- Find The Maximum And Minimum
- Find The Rank Of A Matrix
- Flatten A Matrix
- Getting The Diagonal Of A Matrix
- Invert A Matrix
- Reshape An Array
- Selecting Elements In An Array
- Transpose A Vector Or Matrix
- 1.1??基础知识
- 数据可视化
- Linux
- 6. 1 基础
- Copy Files And Directories
- Delete Files And Directories
- Delete Files And Directories In Current Directory
- Move Files And Directories
- Rename File
- See Disk Drive Space
- Archive And Unarchive Files
- Change Permissions
- Changing Directories
- Check Current Date And Time
- Create Command
- Create Directory
- Create File
- Create Sequential List Of Files And Directories
- Create Symbolic Links
- Exit Terminal Session
- Get Help With A Command
- Get Information On A File
- List Avaliable Commands
- List The Contents Of A Directory
- Multiple Commands On One Line
- Ping Website
- See Free Memory
- See Who Is Logged Into A System
- Select Files Based On Filename
- Synchronize Files And Directories
- Track Route Of Network Traffic
- View A File’s Type
- View A Text File’s Contents
- View Current Working Directory
- View First And Last Parts Of Files
- Zip And Unzip Directories
- Zip And Unzip Files
- 6.2?环境变量
- 6.3??流程控制
- 6.4??输入与输出
- 6.5??进程
- 6.6??搜索
- 6.7??文本
- 6. 1 基础
- 深度学习
- 2.1??Keras
- Adding Dropout
- Convolutional Neural Network
- Feedforward Neural Network For Binary Classification
- Feedforward Neural Network For Multiclass Classification
- Feedforward Neural Networks For Regression
- LSTM Recurrent Neural Network
- Neural Network Early Stopping
- Neural Network Weight Regularization
- Preprocessing Data For Neural Networks
- Save Model Training Progress
- Tuning Neural Network Hyperparameters
- Visualize Loss History
- Visualize Neural Network Architecutre
- Visualize Performance History
- k-Fold Cross-Validating Neural Networks
- 2.2??Keras
- 2.1??Keras
- 正则表达式
- 7.1?模式
- Match A Symbol
- Match A Unicode Character
- Match A Word
- Match Any Character
- Match Any Of A List Of Characters
- Match Any Of A Series Of Options
- Match Any Of A Series Of Words
- Match Dates
- Match Email Addresses
- Match Exact Text
- Match Integers Of Any Length
- Match Text Between HTML Tags
- Match Times
- Match URLs
- Match US Phone Numbers
- Match US and UK Spellings
- Match Words With A Certain Ending
- Match ZIP Codes
- 7.1?模式
- 书籍推荐
Navigation : Python 机器学习 数据可视化 Linux – 基础 — Copy Files And Directories — Delete Files And Directories — Delete Files And Directories In Current Directory — Move Files And Directories — Rename File — See Disk Drive Space — Archive And Unarchive Files — Change Permissions — Changing Directories — Check Current Date And Time — Create Command — Create Directory — Create File — Create Sequential List Of Files And Directories — Create Symbolic Links — Exit Terminal Session — Get Help With A Command — Get Information On A File — List Avaliable Commands — List The Contents Of A Directory — Multiple Commands On One Line — Ping Website — See Free Memory — See Who Is Logged Into A System — Select Files Based On Filename — Synchronize Files And Directories — Track Route Of Network Traffic — View A File’s Type — View A Text File’s Contents — View Current Working Directory — View First And Last Parts Of Files — Zip And Unzip Directories — Zip And Unzip Files – 环境变量 – 流程控制 – 输入与输出 – 进程 – 搜索 – 文本 深度学习 正则表达式 书籍推荐
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