Data Science resources
Newsletters
| # | Link | Description |
|---|---|---|
| 1 | 10 Best | ML & AI newsletters |
| 2 | Alpha Signal | newsletter |
Search engines
| # | Link | Description |
|---|---|---|
| 1 | Refseek | Academic Resource Search |
| 2 | WorldCat | > 20 thousand worldwide libraries |
| 3 | Springer Nature | > 10 million scientific documents |
| 4 | BioLine | scientific bioscience journals |
| 5 | RePeC | Research Papers in Economics |
| 6 | Science.gov | > 200 million indexed articles |
Tutorials
| # | Link | Description |
|---|---|---|
| 1 | Kaggle's | guides and tutorials |
| 1 | Python Robotics | algorithms |
| 2 | The Incredible | PyTorch |
| 3 | Understanding LSTM | Networks |
| 4 | Neural Networks | A 30,000 Feet View for Beginners |
| 5 | The Data Engineering | Handbook |
| 6 | The Breaking Into Data | Handbook |
| 7 | Data Engineering | Practice Problems |
| 8 | Path to Senior Engineer | Handbook |
| 9 | Machine Learning Q and AI | - Questions and Answers |
ML Ops
| # | Link | Description |
|---|---|---|
| 1 | Machine Learning | with Django |
| 2 | MLJAR | Automated ML for Humans |
| 3 | Made With ML | ML for Developers |
| 4 | What is MLOps? | Introduction by OVH |
| 5 | MLOps | Roadmap |
| 6 | 10 repositories | to master MLOps |
| 7 | MLOps | with GitHub |
| 8 | MLOps | with Google |
Blogs
| # | Link | Description |
|---|---|---|
| 1 | Andrej Karpathy's | blog |
| 2 | MonkeyLearn's | blog |
Books online
| # | Link | Description |
|---|---|---|
| 1 | Understanding | Deep Learning |
| 2 | Deep Learning | Book |
| 3 | Data Science notebooks | IPython examples |
Tools
| # | Link | Description |
|---|---|---|
| 1 | Xarray | multi-dimensional arrays |
| 2 | polars | blazingly fast DataFrames |
| 3 | tqdm | a smart progress meter |
| 4 | seaborn | statistical data visualization |
| 5 | Mostly.ai | create synthetic data |
| 6 | SQL Model | databases in Python |
| 7 | WEKA | the workbench for ML |
| 8 | Yellow Brick | Machine Learning Visualization |
| 9 | PyCaret | Low-level Machine Learning |
| 10 | imbalanced-learn | classification with imbalanced classes |
| 11 | Modin | boost Panda's performance |
| 12 | SHAP | explains output of ML model |
| 13 | missingno | missing data visualizations |