Shows notes i've made on my journey to machine_learning_with_tensorflow_on_gcp_specialization
If these notes help 1 person, i'll be happy! This is my attempt to give back to the open source community
Apologies if my earlier course notes aren't well laid out, I improved my note taking as I went on this journey. My favourite courses were 008_ml_ops & 009_ml_pipelines_on_gcp.
Completed in this order
- 001_how_google_does_ml Link to course here
- 002_launching-machine-learning/labs Link to course here
- 003_intro-to-tensorflow/labs Link to course here
- 004_feature-engineering/labs Link to course here
- 005_art_and_science_of_ml Link to course here
- 006_big_data_ml_fundamentals Link to course here
- 007_production_ml_systems Link to course here
- 008_ml_ops Link to course here
- 009_ml_pipelines_on_gcp Link to course here
handy-function- handy function
Note: The pictures below are screenshotted from the lecture notes. If you would like to know more information about a picture, please refer to the picture's src path and it will show which course it came from. There is a associated README within each course that may be helpful.
The pictures below start from 001_how_google_does_ml and finish with 009_ml_pipelines_on_gcp
Goodfit
Overfitting
Underfitting 1/2
Underfitting 2/2
Under represented training dataset
Under represented validation dataset
Under represented validation dataset
Maturity Level 0
Maturity Level 1
Maturity Level 2
