Using Fast Style Transfer library in Tensorflow to add styles from famous paintings to photos as part of Udacity Deep Learning nanodegree trial.
I have finally completed the Data Scientist with Python track in Datacamp which includes the following 20 courses:
- Intro to Python for Data Science
- Intermediate Python for Data Science
- Python Data Science Toolbox (Part 1)
- Python Data Science Toolbox (Part 2)
- Importing Data in Python (Part 1)
- Importing Data in Python (Part 2)
- Cleaning Data in Python
- pandas Foundations
- Manipulating DataFrames with pandas
- Merging DataFrames with pandas
- Introduction to Databases in Python
- Introduction to Data Visualization with Python
- Interactive Data Visualization with Bokeh
- Statistical Thinking in Python (Part 1)
- Statistical Thinking in Python (Part 2)
- Supervised Learning with scikit-learn
- Machine Learning with the Experts: School Budgets
- Unsupervised Learning in Python
- Deep Learning in Python
- Network Analysis in Python (Part 1)
As a bonus I have also completed the Data Analyst and Python Programmer tracks
I was invited to a free Preview of our Self-Driving Car Nanodegree Program and the above is the result of the first project where I learned what you can do with OpenCV (region masking, canny filtering, Hough transform). Quite interesting but currently I prefer to focus on other topics.
The source can be found on Github
Profile of Airbus fleet on flight using the data corresponding to the day 16th of January 2017 (downloaded from ADBSexchange).
The source data is 1.440 JSON files of all aircraft on flight i.e. a file for every 60 seconds. The time scale starts in the hour 0Z i.e. 0:00 UTC
Similar profile of Boeing fleet
Comparison between A320 and B737
The Python source code used to perform the analysis can be found in this github repository.