This repository contains material related to the course 'Adatbányászat_és_Gépi_tanulás' (IK-INFABGTEG) taught by me (Szegedi Gábor) at Eötvös Loránd Science University.
For suggestions and improvements feel free to create a pull request.
I will put the presentation slides here each week, along with the notes to help you with the practical parts.
Consultation hours are on Thursday 10-12. You can find me on the 7th floor of the Northern building in room 7.25.
For questions and out of hours consultation write me an email : wayasam at gmail
The below list should be followed in this order. These cover what we will need for completing this course.
- Python https://www.kaggle.com/learn/python
- Pandas basics https://www.kaggle.com/learn/pandas
- Visualization basics https://www.kaggle.com/learn/data-visualization
- Machine Learning basics https://www.kaggle.com/learn/intro-to-machine-learning
- Some intermediate ML stuff https://www.kaggle.com/learn/intermediate-machine-learning
- Feature engineering https://www.kaggle.com/learn/feature-engineering
+3 that we just touch during the semester, but essential for becoming a Data Scientist
- Deep Learning https://www.kaggle.com/learn/deep-learning
- SQL Introduction https://www.kaggle.com/learn/intro-to-sql
- Advanced SQL https://www.kaggle.com/learn/advanced-sql
Books for helping with the theory
- Deep Learning book by Ian Goodfellow and Yoshua Bengio and Aaron Courville. This is referenced as the goto book for Data Scientist as it covers almost every topic quite deeply. For some of the chapters there are some very good summaries on github. Chapter Summaries and the Book in free online format.
- Introduction to Data Mining by Pang Ning Tan, Michael Steinbach, Anuj Karpatne and Vipin Kumar. Free here
- A General Introduction to Data Analytics by João Mendes Moreira, André C. P. L. F. de Carvalho and Tomáš Horváth Buy here
Credits are due to João Mendes Moreira, Tomáš Horváth and Krisztian Buza for allowing me to use their slides for the creation of these slides.
The content of this repository is licensed under the Creative Commons Attribution 3.0 Unported License.