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    • Python DS Handbook
      • 1. Jupyter Notebook (IPython)
      • 2. Introduction to NumPy
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      • 5. Machine Learning
    • Hands-on Machine Learning
      • I. The Fundamentals - ML
        • 1. The ML Landscape
        • 2. End-to-End ML Project
        • 3. Classification
        • 4. Training Models
        • 5. SVM
        • 6. Decision Trees
        • 7. Ensemble Learning & Random Forests
        • 8. Dimension Reduction
        • 9. Unsupervised Learning
      • II. Neural Networks - DL
    • Deep Learning with Python
      • 1. Fundamentals of DL
      • 2. Deep Learning in Practice
    • Elements of Statistical Learning
    • Math for Machine Learning
      • I. Mathematical Foundations
      • II. Central ML Problems
    • Deep Reinforcement Learning
      • I. Tabular Solution Methods
      • II. Approx. Solution Methods
      • III. Looking Deeper
      • IV. Deep RL
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Machine Learning

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Last updated 28 days ago

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