These are my personal notes taken while following the Udacity AWS Machine Learning Engineer Nanodegree.
The Nanodegree asssumes basic data analysis skills with data science python libraries and databases, and has 4-5 modules that build up on those skills; each module has its corresponding folder in this repository with its guide Markdown file:
- Introduction to Machine Learning:
01_Intro_ML. - Developing your First ML Workflow:
02_ML_Workflow. - Deep Learning Topics with Computer Vision and NLP:
03_DeepLearning. - Operationalizing ML on SageMaker:
04_Operationalizing_SageMaker. - Capstone Project:
05_Capstone_Project
Additionally, it is necessary to submit and pass some projects to get the certification:
- Project 1: TBD.
- Project 2: TBD.
- Project 3: TBD.
- Project 4: TBD.
Finally, also check some of my personal guides on related tools:
- My personal notes on the Udacity Data Science Nanodegree
- My personal notes on the Machine Learning DevOps Engineer
- Deep Learning Methods for CV and NLP:
mxagar/computer_vision_udacity/CVND_Advanced_CV_and_DL.md - Deep Learning Methods for NLP:
mxagar/deep_learning_udacity/DLND_RNNs.md
A regular python environment with the usual data science packages should suffice (i.e., scikit-learn, pandas, matplotlib, etc.); any special/additional packages and their installation commands are introduced in the guides. A recipe to set up a conda environment with my current packages is the following:
# Create the necessary Python environment # NOTE: specific folders might require their own environment # and have their own requirements.txt conda env create -f conda.yaml conda activate mlaws # Dependencies pip-compile requirements.in pip-sync requirements.txt # If we need a new dependency, # add it to requirements.in # (WATCH OUT: try to follow alphabetical order) # And then: pip-compile requirements.in pip-sync requirements.txt
Many of the contents in this repository were created following the Udacity AWS Machine Learning Engineer Nanodegree.
Mikel Sagardia, 2025.
No guarantees.