Notes On Using
Data Science & Artificial Intelligence
To Fight For Something That Matters
I am a data scientist and political scientist with a decade of experience applying statistical learning, artificial intelligence, and software engineering to political, social, and humanitarian efforts -- from election monitoring to disaster relief.
I am currently about to start something big. Stay tuned.
Technical Notes On
Machine Learning & Artificial Intelligence
Years ago I posted my personal technical notes on computational social science, statistics, data science, and scientific programming. Since then the collection has grown and today is visited by thousands daily looking for information on everything from regression assumptions to distributed computing.
If you are looking for more, check out my Machine Learning Flashcards or my book, Machine Learning With Python Cookbook.
Machine Learning
Vectors, Matrices, And Arrays
- Transpose A Vector Or Matrix
- Selecting Elements In An Array
- Reshape An Array
- Invert A Matrix
- Getting The Diagonal Of A Matrix
- Flatten A Matrix
- Find The Rank Of A Matrix
- Find The Maximum And Minimum
- Describe An Array
- Create A Vector
- Create A Sparse Matrix
- Create A Matrix
- Converting A Dictionary Into A Matrix
- Calculate The Trace Of A Matrix
- Calculate The Determinant Of A Matrix
- Calculate The Average, Variance, And Standard Deviation
- Calculate Dot Product Of Two Vectors
- Apply Operations To Elements
- Adding And Subtracting Matrices
Basics
- 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
Preprocessing Structured Data
- 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
Preprocessing Images
Preprocessing Dates And Times
- 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
Feature Engineering
- 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
Model Evaluation
- 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
Trees And Forests
- Adaboost Classifier
- Decision Tree Classifier
- Decision Tree Regression
- Feature Importance
- Feature Selection Using Random Forest
- 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
Deep Learning
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
Python
Basics
- Add Padding Around String
- 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 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
- Try, Except, and Finally
- Unpacking A Tuple
- Unpacking Function Arguments
- Using Named Tuples To Store Data
- any(), all(), max(), min(), sum()
- if and if else
- repr vs. str
- while Statement
Data Wrangling
- 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
- Columns Shared By Two Data Frames
- 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
- Find Unique Values In Pandas Dataframes
- 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
- Join And Merge Pandas Dataframe
- 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
Data Visualization
- 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
Statistics
Scala
- Break A Sequence Into Groups
- Change Data Type
- Chunk Sequence In Equal Sized Groups
- Compare Two Floats
- Create A Range
- Extract Substrings Using Regex
- Filter A Sequence
- Find Largest Key Or Value In A Map
- Flatten Sequence Of Sequences
- For Loop A Map
- For Looping
- Format Numbers As Currency
- If Else
- Increment And Decrement Numbers
- Insert Variables Into Strings
- Iterate Over A Map
- Loop A Collection
- Make Numbers Pretty
- Mapping A Function To A Collection
- Matching Conditions
- Mutable Maps
- N Dimension Arrays
- Partial Functions
- Random Integer Between Two Values
- Replacing Parts Of Strings
- Search A Map
- Search Strings
- Search Strings Using Regex
- Set Operations On Sequences
- Sorting Sequences
- Split Strings
- Try, Catch, Finally
- Variables And Values
- Zip Together Two Lists
Regular Expressions
- 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
SQL
Mathematics
Software Engineering
Cloud Computing
Articles
- Feb 2016 - What I Learned Tracking My Time At Techstars
- Dec 2011 - Health System Destruction During The Mozambican Civil War
- Dec 2011 - Health System Reconstruction In Post-War Kosovo
- Dec 2011 - The Problem Of Rebel Mobilization
- Dec 2011 - The Structure Of Health Systems