50 more profound Kaggle Discussions (tips, tricks, feature engineering, resources) by the top Kaggle Grandmasters
Part 2
Feature Engineering
- Feature Engineering Tricks
- Best Practices for Feature Engineering
- Feature Engineering Ideas
- Avoid Overfitting with Feature Neutralization
Resources
5. Research Trends in Object Detection
6. The Python Graph Gallery: Learning Data Visualization
7. Extensive Resource Compilation for Audio Data
8. Hugging Face course — great for learning NLP
9. SQL interview questions for data scientists
10. Statistics book completely made with cartoons!
11. 13 notebooks to learn Pytorch basic, Alexnet, ResNet, DenSeNet, LeNet and VGG
12. Getting started guide for Image Competitions
13. The Data Scientist Guide to Apache Spark
14. (Some more) time series resources
15. 30 question to test your knowledge of KNN
16. Learning resources for Recommender systems
17. Best of Kaggle Notebooks #4. — Natural Language Processing
18. [Links List] — Everything you need related to BERT in one place (Papers, Articles, Reading, Code).
19. Excellent Pandas Exercise to Learn Pandas
20. Kernel links for Fastai (2019): Practical Deep Learning for coders
21. The Kaggle Book
22. matplotlib : Many Python notebooks to learn data visualization with matplotlib
23. Tutorials for newcomers to data science
Tips
24. (Grand)Mastering your Data Science Notebook Flow
25. Cross validation and splitting
27. Improving code quality with utility scripts
28. the trick to make wonderful classifier
29. Why do people care about balancing classes?
30. Transformers in Time-Series Forecasting
31. Learning Label Correlation by Sequential Models
32. This is why you should use Experiment Tracking Tools for ML
34. Every single Machine Learning course on the internet, ranked by reviews
35. Avoid generic projects on your Data Science portfolio
36. Reduce the size of your train and test data to model more easily
37. Ideas for word embeddings augmentation
38. Beginners, you’ve got to avoid these doubts!
Tutorials
39. 1M rows: …how to read in only some of the data
40. Some tips on preprocessing for GloVe
41. Everything you always wanted to know about BERT (but were afraid to ask)
42. 7 step strategy to get better at Kaggle Competitions’
43. 5 Beginner Friendly Steps to Learn Machine Learning and Data Science with Python
44. Text extraction from a corpus using BERT
45. Tutorial compilation for handling larger datasets
46. Approaching (Almost) Any Machine Learning Problem
47. Easier on Kaggle — Statistics and Machine Learning in Python — great site for ML enthusiasts
48. Should you start learning D3? My breakdown on Data Visualization.
49. useful for beginner: 🐜 how to debug algorithm (not software)
50. [Guide] — How to ensemble object detection models?
