"Data scientists interpret, extrapolate from, and prescribe from data to deliver actionable recommendations."
The Data Science Nanodegree is split into several sections each with their own lecture materials and projects:
- Supervised Learning
- Deep Learning
- Unsupervised Learning
- Data Science Blog Post
- Disaster Response Pipeline (Twitter Webapp)
- Recommendation Engines
- Spark ML
Hence, I have broken up the files numerically according to this order.
Learning Material Files:
- Lecture notes as GIMP xcf files (In part 2 notes are in PDF format)
- Folders containing code implementations
Project Files Notes:
- For
02_deep-learning_project
- Notebooks were written in an GPU enabled workspace. Hence, this file will only work if CUDA is installed on your PC.
- Data images are also too large to be committed onto GitHub
- For
07_capstone_spark-ML_project
- Notebook was written in a Spark enabled workspace and will likely not work if you haven't downloaded and installed spark on your local machine.
- Data Used is only a subset of the whole gigabyte size dataset, also wont be commited onto GitHub
See project subdirectories