Study notes, experiments, and important concepts in machine learning and deep learning.
This repo is primarily for my own learning journey, but it may also serve as a reference for others exploring ML.
- A collection of Jupyter notebooks covering:
- Statistical machine learning (scikit-learn)
- Deep learning with TensorFlow
- Deep learning with PyTorch
- Focused on learning, practicing, and documenting core ideas.
- Serves as a personal knowledge base of important ML concepts, implementations, and experiments.
- Learn by implementing key ML/DL techniques from scratch and with libraries.
- Keep a record of concepts I find useful or important.
- Build a resource I can return to when revisiting ideas.
- Share my learning process with others who might benefit.
- https://www.udemy.com/course/pytorch-for-deep-learning
- https://www.udemy.com/course/tensorflow-developer-certificate-machine-learning-zero-to-mastery
- https://www.udemy.com/course/complete-machine-learning-and-data-science-zero-to-mastery
- https://www.udemy.com/course/credit-risk-modeling-in-python
Clone the repo, set up your environment (scikit-learn / TensorFlow / PyTorch), and run the notebooks in Jupyter Lab.