Dependency Matrix of Python Libraries for Machine Learning.

1. Python Basics: Foundational knowledge is required before delving into any other libraries.
   

2. NumPy: Provides support for arrays and matrices, essential for mathematical computations.
   

3. Pandas: Builds on NumPy to offer powerful data manipulation and analysis capabilities.
   

4. Matplotlib: Basic plotting library, useful for data visualization.
   

5. Seaborn: Built on Matplotlib, provides more sophisticated visualization tools.
   

6. Scikit-learn: Uses NumPy, Pandas, and visualization libraries for machine learning tasks.
   

7. SciPy: Builds on NumPy, offering advanced scientific computations.
   

8. Statsmodels: For statistical modeling, it requires an understanding of NumPy, Pandas, and SciPy.
   

9. TensorFlow: For deep learning; it requires knowledge of NumPy and optionally integrates with Keras.
   

10. Keras: High-level API for TensorFlow, simplifying the construction of neural networks.
    

11. PyTorch: An alternative to TensorFlow for deep learning, with a different approach to model building.
    

12. OpenCV: For computer vision tasks, relies on NumPy for image manipulations.
    

13. NLTK: Natural language processing library, more domain-specific, doesn't heavily rely on earlier libraries.
    

14. spaCy: Another NLP library, that works well with Pandas for data handling.
    

15. XGBoost: An efficient gradient boosting library, that works well with NumPy and Pandas.
    

16. LightGBM: Similar to XGBoost, also used for gradient-boosting tasks.
    

17. Fastai: Built on PyTorch, provides high-level abstractions for deep learning.

This learning path and dependency matrix provide a structured approach to mastering the libraries essential for machine learning in Python.