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arcgis-python-api

Documentation and samples for ArcGIS API for Python

awp

bokeh

Interactive Data Visualization in the browser, from Python

capstone-final

coursera_capstone

Repository for the Coursera Capstone Project

covid-19

Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE

cs-video-courses

List of Computer Science courses with video lectures.

csharp-notebooks

Get started learning C# with C# notebooks powered by .NET Interactive and VS Code.

dashboards

[RETIRED] See Voilà as a supported replacement

data-analysis-and-machine-learning-projects

Repository of teaching materials, code, and data for my data analysis and machine learning projects.

data-science-algorithms-in-a-week

Data Science Algorithms in a Week, published by Packt

data-science-projects-with-python

A Case Study Approach to Successful Data Science Projects Using Python, Pandas, and Scikit-Learn

deep-learning-explained

This repository contains the lab files for Microsoft course DAT236x: Deep Learning Explained

fileinfoapp

Tutorial for creating Autodesk Navisworks Add-Ins.

folium

Python Data. Leaflet.js Maps.

forgeviewer_embed_in_powerbi_report

hands-on-data-analysis-with-pandas

Materials for following along with Hands-On Data Analysis with Pandas.

intel_deep_learning

This course provides an introduction to deep learning on modern Intel® architecture. Deep learning has gained significant attention in the industry by achieving state of the art results in computer vision and natural language processing. By the end of this course, students will have a firm understanding of: Techniques, terminology, and mathematics of deep learning Fundamental neural network architectures, feedforward networks, convolutional networks, and recurrent networks How to appropriately build and train these models Various deep learning applications How to use pre-trained models for best results The course is structured around 12 weeks of lectures and exercises. Each week requires three hours to complete.

intel_machine_learning

This course provides an overview of machine learning fundamentals on modern Intel® architecture. Topics covered include: Reviewing the types of problems that can be solved Understanding building blocks Learning the fundamentals of building models in machine learning Exploring key algorithms By the end of this course, students will have practical knowledge of: Supervised learning algorithms Key concepts like under- and over-fitting, regularization, and cross-validation How to identify the type of problem to be solved, choose the right algorithm, tune parameters, and validate a model The course is structured around 12 weeks of lectures and exercises. Each week requires three hours to complete. The exercises are implemented in Python*, so familiarity with the language is encouraged (you can learn along the way).

learning-threejs

Code repository for the examples from the Packt book "Learning Threejs"

lego

Analysis of some lego data from https://rebrickable.com/downloads/

machine-learning-projects

This repository consists of all my Machine Learning Projects.

machine-learning-with-python

Python code for common Machine Learning Algorithms

mydriving

Building IoT or Mobile solutions are fun and exciting. This year for Build, we wanted to show the amazing scenarios that can come together when these two are combined. So, we went and developed a sample application. MyDriving uses a wide range of Azure services to process and analyze car telemetry data for both real-time insights and long-term patterns and trends. The following features are supported in the current version of the mobile app.

or-tools

Google's Operations Research tools:

pandas-cookbook

Recipes for using Python's pandas library

pandas_exercises

Practice your pandas skills!

power-bi

powerbi-desktop-samples

Power BI Desktop sample files for the monthly release. Here you can find the PBIX files used in the monthly release videos.

practical-data-science-cookbook-second-edition

Practical Data Science Cookbook, Second Edition, published by Packt