Essential Python Libraries to build your career in Data Science 📊👇

1. NumPy:
- Efficient numerical operations and array manipulation.

2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).

3. Matplotlib:
- 2D plotting library for creating visualizations.

4. Seaborn:
- Statistical data visualization built on top of Matplotlib.

5. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.

6. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.

7. PyTorch:
- Deep learning library, particularly popular for neural network research.

8. SciPy:
- Library for scientific and technical computing.

9. Statsmodels:
- Statistical modeling and econometrics in Python.

10. NLTK (Natural Language Toolkit):
- Tools for working with human language data (text).

11. Gensim:
- Topic modeling and document similarity analysis.

12. Keras:
- High-level neural networks API, running on top of TensorFlow.

13. Plotly:
- Interactive graphing library for making interactive plots.

14. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.

15. OpenCV:
- Library for computer vision tasks.


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A model can have high accuracy and still be unsafe.

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Positive prediction:
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Negative prediction:
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False negative cost:
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🔗 Research next:
Scikit-learn Model Evaluation

Scikit-learn Probability Calibration

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Free challenge platforms

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• Link: https://www.kaggle.com/competitions

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Top 1% proof-of-work portfolio

🎯 TURN ONE COMPETITION INTO A PORTFOLIO PROJECT

Do not upload only your final score.

Create a GitHub repo with:

1. Problem statement
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2. Dataset
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3. Approach
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Weekly action

✅ This week:
1. Join one beginner competition.
2. Build a simple baseline model.
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