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Atma's blog

Home
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- Cloud
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- Machine Learning
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  - Core Concepts
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    Scikit Learn syntax
    
    Understanding Gradient Descent
    
    A primer on linear algebra
    
    Naive Bayes classification - Sklearn
  - Generalized linear models
    Generalized linear models
    
    Linear regression concepts
    
    Solving multivariate linear regression
    
    Analytical vs Gradient Descent methods for linear regression
    
    Logistic regression concepts
    
    Model regularization
  - Applications
    Applications
    
    Implementing Gradient Descent for Linear Regression
    
    Applying Linear Regression with scikit-learn and statmodels
    
    Implementing Gradient Descent for Logistic Regression
    
    MNIST digits classification using Logistic regression in Scikit-Learn
- Statistics
  Statistics
- Distributed Computing
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- Spatial Analysis
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  - Spatial data structures
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    Python - data types
    
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    Python conditional execution
    
    Python - Functions
    
    Iteration
    
    Exception handling
    
    Classes
    
    Mutability
    
    Memory
    
    Python language optimizations
    
    Conda - Env and Package manager for Python
    
    Mamba package manager
  - Golang
    
    Golang
    
    Basics
    
    Datatypes
    
    Control structures
    
    Goroutines
  - Octave
    Octave
    
    Loops, conditional execution, functions
    
    Plotting
  - R programming
    R programming
    
    R - datatypes, variables
  - Front end development
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    HTML
    
    Javascript
    
    Web development
- Data analysis
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  - NumPy
    NumPy
    
    Numpy - Array creation, inspection and manipulation
    
    Array operations - slicing, dicing, searching
  - Pandas
    Pandas
    
    Pandas - DataFrame creation and exploration
    
    Pandas - Indices, Sorting, Merging
    
    Pandas - Unique, IsNull, Time series
    
    Data visualization with Pandas
  - Matplotlib
    Matplotlib
    
    Matplotlib - intro, subplots
    
    Matplotlib - working with axes
    
    Geographical plotting with matplotlib
  - Seaborn
    Seaborn
    
    Seaborn - exploring distributions
    
    Seaborn - categorical plotting
    
    Seaborn - matrix and regression plots
    
    Seaborn - grids and customization
  - Plotly
    Plotly
    
    Plotly - getting started
    
    Plotly - plotting with Pandas
    
    Plotly - geographical plotting
  - SQL
    SQL
    
    Intro, CRUD operations
    
    Query operators
    
    SQL In Depth
    
    Joins
    
    SQL problem sets 1
- Geospatial
  Geospatial
  - GDAL & OGR
    GDAL & OGR
    
    Intro
    
    GDAL - Inspecting rasters
    
    GDAL - mosaicking, warping, calculating, pansharpening
    
    GDAL - interacting with WMS
    
    OGR - inspection, ogr2ogr
  - Geopandas
    Geopandas
    
    GeoPandas - intro, plotting
    
    Geopandas - geocoding, interactive plotting
    
    Geopandas - spatial operations
    
    Geopandas - data classification, PySAL, OMS
  - Rasterio
    Rasterio
    
    Rasterio - inspection, plotting raster data
    
    Rasterio - hyperspectral data
  - Xarray
    Xarray
    
    Xarray - an introduction
    
    Xarray - multidimensional science data
  - PostGIS
    PostGIS
    
    Intro, spatial queries
    
    PostGIS - Using SQLAlchemy, GeoAlchemy, and GeoPandas
- Tools
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Blog

Python Scripting for ArcGIS Pro

Reviewed and contributed to the 2nd edition of Python Scripting for ArcGIS Pro book.

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