O’REILLY | Data Science Bookcamp, Video Edition [FCO]

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Uploaded :15 May, 2022
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O’REILLY | Data Science Bookcamp, Video Edition [FCO]

  • O’REILLY | Data Science Bookcamp, Video Edition [FCO]6.4 GB
    • 1 - Case study 1 - Finding the winning strategy in a card game.mp46.9 MB
    • 10 - Chapter 3. Using permutations to shuffle cards.mp435.4 MB
    • 100 - Chapter 20. Network-driven supervised machine learning.mp449.0 MB
    • 101 - Chapter 20. The basics of supervised machine learning.mp449.2 MB
    • 102 - Chapter 20. Measuring predicted label accuracy, Part 1.mp437.3 MB
    • 103 - Chapter 20. Measuring predicted label accuracy, Part 2.mp455.2 MB
    • 104 - Chapter 20. Optimizing KNN performance.mp435.7 MB
    • 105 - Chapter 20. Running a grid search using scikit-learn.mp439.3 MB
    • 106 - Chapter 20. Limitations of the KNN algorithm.mp463.2 MB
    • 107 - Chapter 21. Training linear classifiers with logistic regression.mp458.3 MB
    • 108 - Chapter 21. Training a linear classifier, Part 1.mp443.5 MB
    • 109 - Chapter 21. Training a linear classifier, Part 2.mp473.3 MB
    • 11 - Chapter 4. Case study 1 solution.mp434.3 MB
    • 110 - Chapter 21. Improving linear classification with logistic regression, Part 1.mp443.4 MB
    • 111 - Chapter 21. Improving linear classification with logistic regression, Part 2.mp443.1 MB
    • 112 - Chapter 21. Training linear classifiers using scikit-learn.mp449.6 MB
    • 113 - Chapter 21. Measuring feature importance with coefficients.mp493.1 MB
    • 114 - Chapter 22. Training nonlinear classifiers with decision tree techniques.mp465.2 MB
    • 115 - Chapter 22. Training a nested if_else model using two features.mp453.3 MB
    • 116 - Chapter 22. Deciding which feature to split on.mp457.2 MB
    • 117 - Chapter 22. Training if_else models with more than two features.mp457.8 MB
    • 118 - Chapter 22. Training decision tree classifiers using scikit-learn.mp451.9 MB
    • 119 - Chapter 22. Studying cancerous cells using feature importance.mp459.3 MB
    • 12 - Chapter 4. Optimizing strategies using the sample space for a 10-card deck.mp447.1 MB
    • 120 - Chapter 22. Improving performance using random forest classification.mp457.4 MB
    • 121 - Chapter 22. Training random forest classifiers using scikit-learn.mp453.0 MB
    • 122 - Chapter 23. Case study 5 solution.mp432.9 MB
    • 123 - Chapter 23. Exploring the experimental observations.mp439.0 MB
    • 124 - Chapter 23. Training a predictive model using network features, Part 1.mp452.6 MB
    • 125 - Chapter 23. Training a predictive model using network features, Part 2.mp453.9 MB