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Data Science Bookcamp, video edition
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[TutsNode.com] - Data Science Bookcamp, video edition (Size: 6.50 GB) (Files: 257)
[TutsNode.com] - Data Science Bookcamp, video edition
113 - Chapter 21. Measuring feature importance with coefficients.mp4
71 - Chapter 15. Clustering texts by topic, Part 2.mp4
66 - Chapter 15. Vectorizing documents using scikit-learn.mp4
86 - Case study 5 - Predicting future friendships from social network data.mp4
23 - Chapter 7. Data dredging - Coming to false conclusions through oversampling.mp4
14 - Chapter 5. Basic probability and statistical analysis using SciPy.mp4
98 - Chapter 19. Community detection using Markov clustering, Part 2.mp4
93 - Chapter 19. Dynamic graph theory techniques for node ranking and social network analysis.mp4
87 - Chapter 18. An introduction to graph theory and network analysis.mp4
16 - Chapter 5. Variance as a measure of dispersion.mp4
70 - Chapter 15. Clustering texts by topic, Part 1.mp4
109 - Chapter 21. Training a linear classifier, Part 2.mp4
81 - Chapter 17. Filtering jobs by relevance.mp4
44 - Chapter 12. Visualizing and clustering the extracted location data.mp4
84 - Chapter 17. Exploring clusters at alternative values of K.mp4
42 - Chapter 11. Limitations of the GeoNamesCache library.mp4
36 - Chapter 10. Clustering based on non-Euclidean distance.mp4
64 - Chapter 14. Efficient dimension reduction using SVD and scikit-learn.mp4
22 - Chapter 7. Assessing the divergence between sample mean and population mean.mp4
82 - Chapter 17. Clustering skills in relevant job postings.mp4
5 - Chapter 2. Comparing multiple coin-flip probability distributions.mp4
114 - Chapter 22. Training nonlinear classifiers with decision tree techniques.mp4
58 - Chapter 14. Dimension reduction using PCA and scikit-learn.mp4
20 - Chapter 6. Computing the area beneath a normal curve.mp4
128 - Chapter 23. Interpreting the trained model.mp4
106 - Chapter 20. Limitations of the KNN algorithm.mp4
76 - Chapter 16. The structure of HTML documents.mp4
41 - Chapter 11. Location tracking using GeoNamesCache.mp4
126 - Chapter 23. Adding profile features to the model.mp4
55 - Chapter 14. Dimension reduction of matrix data.mp4
33 - Chapter 10. Clustering data into groups.mp4
34 - Chapter 10. K-means - A clustering algorithm for grouping data into K central groups.mp4
3 - Chapter 1. Problem 2 - Analyzing multiple die rolls.mp4
69 - Chapter 15. Computing similarities across large document datasets.mp4
97 - Chapter 19. Community detection using Markov clustering, Part 1.mp4
80 - Chapter 17. Exploring the HTML for skill descriptions.mp4
119 - Chapter 22. Studying cancerous cells using feature importance.mp4
72 - Chapter 15. Visualizing text clusters.mp4
74 - Chapter 15. Using subplots to display multiple word clouds, Part 2.mp4
17 - Chapter 6. Making predictions using the central limit theorem and SciPy.mp4
40 - Chapter 11. Visualizing maps.mp4
107 - Chapter 21. Training linear classifiers with logistic regression.mp4
99 - Chapter 19. Uncovering friend groups in social networks.mp4
117 - Chapter 22. Training if_else models with more than two features.mp4
8 - Chapter 3. Deriving probabilities from histograms.mp4
90 - Chapter 18. Utilizing undirected graphs to optimize the travel time between towns.mp4
120 - Chapter 22. Improving performance using random forest classification.mp4
116 - Chapter 22. Deciding which feature to split on.mp4
2 - Chapter 1. Computing probabilities using Python This section covers.mp4
67 - Chapter 15. Ranking words by both post frequency and count, Part 1.mp4
103 - Chapter 20. Measuring predicted label accuracy, Part 2.mp4
19 - Chapter 6. Determining the mean and variance of a population through random sampling.mp4
59 - Chapter 14. Clustering 4D data in two dimensions.mp4
125 - Chapter 23. Training a predictive model using network features, Part 2.mp4
4 - Chapter 2. Plotting probabilities using Matplotlib.mp4
24 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 1.mp4
115 - Chapter 22. Training a nested if_else model using two features.mp4
89 - Chapter 18. Analyzing web networks using NetworkX, Part 2.mp4
9 - Chapter 3. Computing histograms in NumPy.mp4
121 - Chapter 22. Training random forest classifiers using scikit-learn.mp4
25 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 2.mp4
124 - Chapter 23. Training a predictive model using network features, Part 1.mp4
35 - Chapter 10. Using density to discover clusters.mp4
118 - Chapter 22. Training decision tree classifiers using scikit-learn.mp4
73 - Chapter 15. Using subplots to display multiple word clouds, Part 1.mp4
112 - Chapter 21. Training linear classifiers using scikit-learn.mp4
101 - Chapter 20. The basics of supervised machine learning.mp4
92 - Chapter 18. Computing the fastest travel time between nodes, Part 2.mp4
100 - Chapter 20. Network-driven supervised machine learning.mp4
52 - Chapter 13. Basic matrix operations, Part 1.mp4
50 - Chapter 13. Using normalization to improve TF vector similarity.mp4
95 - Chapter 19. Deriving PageRank centrality from probability theory.mp4
68 - Chapter 15. Ranking words by both post frequency and count, Part 2.mp4
54 - Chapter 13. Computational limits of matrix multiplication.mp4
61 - Chapter 14. Computing principal components without rotation.mp4
7 - Chapter 3. Computing confidence intervals using histograms and NumPy arrays.mp4
65 - Chapter 15. NLP analysis of large text datasets.mp4
12 - Chapter 4. Optimizing strategies using the sample space for a 10-card deck.mp4
78 - Chapter 16. Parsing HTML using Beautiful Soup, Part 2.mp4
38 - Chapter 11. Geographic location visualization and analysis.mp4
62 - Chapter 14. Extracting eigenvectors using power iteration, Part 1.mp4
96 - Chapter 19. Computing PageRank centrality using NetworkX.mp4
49 - Chapter 13. Vectorizing texts using word counts.mp4
47 - Chapter 13. Simple text comparison.mp4
26 - Chapter 7. Permutation testing - Comparing means of samples when the population parameters are unknown.mp4
31 - Chapter 9. Determining statistical significance.mp4
108 - Chapter 21. Training a linear classifier, Part 1.mp4
110 - Chapter 21. Improving linear classification with logistic regression, Part 1.mp4
111 - Chapter 21. Improving linear classification with logistic regression, Part 2.mp4
127 - Chapter 23. Optimizing performance across a steady set of features.mp4
48 - Chapter 13. Replacing words with numeric values.mp4
51 - Chapter 13. Using unit vector dot products to convert between relevance metrics.mp4
83 - Chapter 17. Investigating the technical skill clusters.mp4
85 - Chapter 17. Analyzing the 700 most relevant postings.mp4
27 - Chapter 8. Analyzing tables using Pandas.mp4
37 - Chapter 10. Analyzing clusters using Pandas.mp4
77 - Chapter 16. Parsing HTML using Beautiful Soup, Part 1.mp4
29 - Chapter 8. Saving and loading table data.mp4
94 - Chapter 19. Computing travel probabilities using matrix multiplication.mp4
75 - Chapter 16. Extracting text from web pages.mp4
105 - Chapter 20. Running a grid search using scikit-learn.mp4
21 - Chapter 7. Statistical hypothesis testing.mp4
123 - Chapter 23. Exploring the experimental observations.mp4
56 - Chapter 14. Reducing dimensions using rotation, Part 1.mp4
28 - Chapter 8. Retrieving table rows.mp4
57 - Chapter 14. Reducing dimensions using rotation, Part 2.mp4
79 - Chapter 17. Case study 4 solution.mp4
102 - Chapter 20. Measuring predicted label accuracy, Part 1.mp4
15 - Chapter 5. Mean as a measure of centrality.mp4
6 - Chapter 3. Running random simulations in NumPy.mp4
46 - Chapter 13. Measuring text similarities.mp4
104 - Chapter 20. Optimizing KNN performance.mp4
10 - Chapter 3. Using permutations to shuffle cards.mp4
43 - Chapter 12. Case study 3 solution.mp4
63 - Chapter 14. Extracting eigenvectors using power iteration, Part 2.mp4
11 - Chapter 4. Case study 1 solution.mp4
30 - Chapter 9. Case study 2 solution.mp4
39 - Chapter 11. Plotting maps using Cartopy.mp4
122 - Chapter 23. Case study 5 solution.mp4
91 - Chapter 18. Computing the fastest travel time between nodes, Part 1.mp4
18 - Chapter 6. Comparing two sampled normal curves.mp4
13 - Case study 2 - Assessing online ad clicks for significance.mp4
88 - Chapter 18. Analyzing web networks using NetworkX, Part 1.mp4
60 - Chapter 14. Limitations of PCA.mp4
53 - Chapter 13. Basic matrix operations, Part 2.mp4
45 - Case study 4 - Using online job postings to improve your data science resume.mp4
1 - Case study 1 - Finding the winning strategy in a card game.mp4
32 - Case study 3 - Tracking disease outbreaks using news headlines.mp4
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