[FreeCoursesOnline Me] O`REILLY - Data Science Bookcamp, video edition
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| Torrent Hash : | 68F1B06F13A8D2DAA4491EE1D44B6731AC249612 |
| Torrent Added : | 1 Year+ in Other - Other |
| Torrent Size : | 6.44 GB |
[FreeCoursesOnline Me] O`REILLY - Data Science Bookcamp, video edition
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Torrent File Content (133 files)
[FreeCoursesOnline.Me] O`REILLY - Data Science Bookcamp, video edition
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1 - Case study 1 - Finding the winning strategy in a card game.mp4 -
6.89 MB
10 - Chapter 3. Using permutations to shuffle cards.mp4 -
35.4 MB
100 - Chapter 20. Network-driven supervised machine learning.mp4 -
48.95 MB
101 - Chapter 20. The basics of supervised machine learning.mp4 -
49.2 MB
102 - Chapter 20. Measuring predicted label accuracy, Part 1.mp4 -
37.28 MB
103 - Chapter 20. Measuring predicted label accuracy, Part 2.mp4 -
55.24 MB
104 - Chapter 20. Optimizing KNN performance.mp4 -
35.68 MB
105 - Chapter 20. Running a grid search using scikit-learn.mp4 -
39.33 MB
106 - Chapter 20. Limitations of the KNN algorithm.mp4 -
63.16 MB
107 - Chapter 21. Training linear classifiers with logistic regression.mp4 -
58.26 MB
108 - Chapter 21. Training a linear classifier, Part 1.mp4 -
43.52 MB
109 - Chapter 21. Training a linear classifier, Part 2.mp4 -
73.26 MB
11 - Chapter 4. Case study 1 solution.mp4 -
34.27 MB
110 - Chapter 21. Improving linear classification with logistic regression, Part 1.mp4 -
43.42 MB
111 - Chapter 21. Improving linear classification with logistic regression, Part 2.mp4 -
43.12 MB
112 - Chapter 21. Training linear classifiers using scikit-learn.mp4 -
49.64 MB
113 - Chapter 21. Measuring feature importance with coefficients.mp4 -
93.13 MB
114 - Chapter 22. Training nonlinear classifiers with decision tree techniques.mp4 -
65.2 MB
115 - Chapter 22. Training a nested if_else model using two features.mp4 -
53.25 MB
116 - Chapter 22. Deciding which feature to split on.mp4 -
57.23 MB
117 - Chapter 22. Training if_else models with more than two features.mp4 -
57.79 MB
118 - Chapter 22. Training decision tree classifiers using scikit-learn.mp4 -
51.86 MB
119 - Chapter 22. Studying cancerous cells using feature importance.mp4 -
59.29 MB
12 - Chapter 4. Optimizing strategies using the sample space for a 10-card deck.mp4 -
47.1 MB
120 - Chapter 22. Improving performance using random forest classification.mp4 -
57.38 MB
121 - Chapter 22. Training random forest classifiers using scikit-learn.mp4 -
52.96 MB
122 - Chapter 23. Case study 5 solution.mp4 -
32.94 MB
123 - Chapter 23. Exploring the experimental observations.mp4 -
38.99 MB
124 - Chapter 23. Training a predictive model using network features, Part 1.mp4 -
52.59 MB
125 - Chapter 23. Training a predictive model using network features, Part 2.mp4 -
53.87 MB
126 - Chapter 23. Adding profile features to the model.mp4 -
62.03 MB
127 - Chapter 23. Optimizing performance across a steady set of features.mp4 -
42.55 MB
128 - Chapter 23. Interpreting the trained model.mp4 -
64.17 MB
13 - Case study 2 - Assessing online ad clicks for significance.mp4 -
31.4 MB
14 - Chapter 5. Basic probability and statistical analysis using SciPy.mp4 -
76.23 MB
15 - Chapter 5. Mean as a measure of centrality.mp4 -
36.58 MB
16 - Chapter 5. Variance as a measure of dispersion.mp4 -
73.89 MB
17 - Chapter 6. Making predictions using the central limit theorem and SciPy.mp4 -
58.61 MB
18 - Chapter 6. Comparing two sampled normal curves.mp4 -
31.46 MB
19 - Chapter 6. Determining the mean and variance of a population through random sampling.mp4 -
55.19 MB
2 - Chapter 1. Computing probabilities using Python This section covers.mp4 -
56.75 MB
20 - Chapter 6. Computing the area beneath a normal curve.mp4 -
64.57 MB
21 - Chapter 7. Statistical hypothesis testing.mp4 -
39.19 MB
22 - Chapter 7. Assessing the divergence between sample mean and population mean.mp4 -
68.3 MB
23 - Chapter 7. Data dredging - Coming to false conclusions through oversampling.mp4 -
79.88 MB
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