| Machine Learning Scientist with Python Modules | Start | End | Notes |
|--------------------------------------------------------- |------- |----- |------- |
| Supervised Learning with scikit-learn | 12/14 | | lectures seem to follow this [book](https://github.com/amueller/introduction_to_ml_with_python) closely, which I have |
| Unsupervised Learning in Python | | | |
| Linear Classifiers in Python | | | |
| Machine Learning with Tree-Based Models in Python | | | |
| Extreme Gradient Boosting with XGBoost | | | |
| Cluster Analysis in Python | | | |
| Dimensionality Reduction in Python | | | |
| Preprocessing for Machine Learning in Python | | | |
| Machine Learning for Time Series Data in Python | | | |
| Feature Engineering for Machine Learning in Python | | | |
| Model Validation in Python | | | |
| Introduction to Natural Language Processing in Python | | | |
| Feature Engineering for NLP in Python | | | |
| Introduction to TensorFlow in Python | | | |
| Introduction to Deep Learning in Python | | | |
| Introduction to Deep Learning with Keras | | | |
| Advanced Deep Learning with Keras | | | |
| Image Processing in Python | 12/14 | | Img, Scikit-image |
| Image Processing with Keras in Python | | | Img |
| Hyperparameter Tuning in Python | | | |
| Introduction to PySpark | | | |
| Machine Learning with PySpark | | | |
| Winning a Kaggle Competition in Python | | | |
| [Applying Hugging Face Machine Learning Pipelines in Python](https://www.educative.io/courses/hugging-face-machine-learning-pipelines-python) | 1/3 | | |
├──Rosalind Problem ID Folder - Solutions to problems folder
| ├── Readme.md - Documentation about the solution
│ ├── *.py files - Python files
│ └── Data files - Input and output files
- Working through Rosalind.info -> "Bioinformatics Stronghold" problems https://rosalind.info/problems/list-view/