Projects
My name is Dimitar Petrov and I am a data science and machine learning engineer. This GitHub repository present some of the projects that I have worked on.
You can find more details and code for each project in their respective folders in this repository. 🤓
These are the practical and theoretical textbooks that I have studied in the last three years and that form half of the foundation of my knowledge in Data Science, Machine Learning, Deep Learning and Statistics. 🤓
- Data Science from Scratch by Joel Grus
- The hundred-page machine learning book (Burkov, Andriy)
- Deep Learning (Ian Goodfellow, Yoshua Bengio, Aaron Courville)
- Inside Deep Learning Math, Algorithms, Models (Final Release) (Edward Raff)
- Grokking Deep Learning (Andrew W. Trask)
- Automated Machine Learning in Action by Qingquan Song, Haifeng Jin, Xia Hu -
- Inside Deep Learning Math, Algorithms, Models (Final Release) (Edward Raff)
- Practical-mlops-operationalizing-machine-learning-models by Noah Gift & Alfredo Deza
- Data Science bookcamp five real-world python projects by Leonard Apeltsin
- Introductory Statistics (10th Edition) (Neil A. Weiss)
- Mathematical and Statistical Methods by Dirk P. Kroese, Zdravko I. Botev, Thomas Taimre, Radislav Vaisman
- Cloud Computing with AWS by Mishra P.
- Introducing-MLOps by Mark Treveil & the Dataiku Team