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| 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/