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Tricking Data Science
Ⓒ Tricking Data Science
1. Pure Python
1.1. Strings
1.2. Dictionaries
1.3. Control flow
1.4. Built-in libraries
1.5. Utility libraries
2. Data Manipulation
2.1. Importing data
2.2. Pandas - neat tricks
2.3. Getting datasets in code
2.4. Time series data manipulation
3. Data Visualization
3.1. Matplotlib
3.2. Seaborn
3.3. Other cool dataviz libraries
4. Machine Learning
4.1. Feature engineering
4.2. Sklearn - neat tricks
4.3. Model explainability
4.4. Codeless advice
4.5. Subtle tricks for ML
4.6. Ensembling
4.7. Time series forecasting
5. MLops
5.1. Data version control
5.2. Packaging ML projects
5.3. Model deployment
5.4. Git
6. Ecosystem
6.1. JupyterLab
6.2. Utility libraries
7. Statistics
7.1. Statistics concepts
7.2. Numpy
8. Natural Language Processing (NLP)
8.1. spaCy
9. Gem Resources
9.1. Pure Python
9.2. General deep learning
9.3. Statistics
9.4. Math
9.5. Datasets
9.6. Data visualization
9.7. MLOps
9.8. Classic ML
9.9. Command line (CLI)
9.10. Explainable AI
9.11. Natural Language Processing (NLP)
9.12. Arbitrary resources
10. The Daily Julia Series
10.2. Installing Julia
Binder
repository
open issue
.ipynb
.pdf
Contents
8.1.1. Predicting named entities (NER)
spaCy
Contents
8.1.1. Predicting named entities (NER)
8.1.
spaCy
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8.1.1.
Predicting named entities (NER)
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Named Entitiy Recognition is ridiculously easy in spaCy. It is even cooler to visualize them👇
