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O�REILLY | Data Science Bookcamp, Video Edition ...
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O�REILLY | Data Science Bookcamp, Video Edition [FCO] Torrent content (File list)
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| 1 - Case study 1 - Finding the winning strategy in a card game.mp4 6.9 MB |
| 10 - Chapter 3. Using permutations to shuffle cards.mp4 35.4 MB |
| 100 - Chapter 20. Network-driven supervised machine learning.mp4 49.0 MB |
| 101 - Chapter 20. The basics of supervised machine learning.mp4 49.2 MB |
| 102 - Chapter 20. Measuring predicted label accuracy, Part 1.mp4 37.3 MB |
| 103 - Chapter 20. Measuring predicted label accuracy, Part 2.mp4 55.2 MB |
| 104 - Chapter 20. Optimizing KNN performance.mp4 35.7 MB |
| 105 - Chapter 20. Running a grid search using scikit-learn.mp4 39.3 MB |
| 106 - Chapter 20. Limitations of the KNN algorithm.mp4 63.2 MB |
| 107 - Chapter 21. Training linear classifiers with logistic regression.mp4 58.3 MB |
| 108 - Chapter 21. Training a linear classifier, Part 1.mp4 43.5 MB |
| 109 - Chapter 21. Training a linear classifier, Part 2.mp4 73.3 MB |
| 11 - Chapter 4. Case study 1 solution.mp4 34.3 MB |
| 110 - Chapter 21. Improving linear classification with logistic regression, Part 1.mp4 43.4 MB |
| 111 - Chapter 21. Improving linear classification with logistic regression, Part 2.mp4 43.1 MB |
| 112 - Chapter 21. Training linear classifiers using scikit-learn.mp4 49.6 MB |
| 113 - Chapter 21. Measuring feature importance with coefficients.mp4 93.1 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 53.3 MB |
| 116 - Chapter 22. Deciding which feature to split on.mp4 57.2 MB |
| 117 - Chapter 22. Training if_else models with more than two features.mp4 57.8 MB |
| 118 - Chapter 22. Training decision tree classifiers using scikit-learn.mp4 51.9 MB |
| 119 - Chapter 22. Studying cancerous cells using feature importance.mp4 59.3 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 57.4 MB |
| 121 - Chapter 22. Training random forest classifiers using scikit-learn.mp4 53.0 MB |
| 122 - Chapter 23. Case study 5 solution.mp4 32.9 MB |
| 123 - Chapter 23. Exploring the experimental observations.mp4 39.0 MB |
| 124 - Chapter 23. Training a predictive model using network features, Part 1.mp4 52.6 MB |
| 125 - Chapter 23. Training a predictive model using network features, Part 2.mp4 53.9 MB |
| 126 - Chapter 23. Adding profile features to the model.mp4 62.0 MB |
| 127 - Chapter 23. Optimizing performance across a steady set of features.mp4 42.6 MB |
| 128 - Chapter 23. Interpreting the trained model.mp4 64.2 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 76.2 MB |
| 15 - Chapter 5. Mean as a measure of centrality.mp4 36.6 MB |
| 16 - Chapter 5. Variance as a measure of dispersion.mp4 73.9 MB |
| 17 - Chapter 6. Making predictions using the central limit theorem and SciPy.mp4 58.6 MB |
| 18 - Chapter 6. Comparing two sampled normal curves.mp4 31.5 MB |
| 19 - Chapter 6. Determining the mean and variance of a population through random sampling.mp4 55.2 MB |
| 2 - Chapter 1. Computing probabilities using Python This section covers.mp4 56.8 MB |
| 20 - Chapter 6. Computing the area beneath a normal curve.mp4 64.6 MB |
| 21 - Chapter 7. Statistical hypothesis testing.mp4 39.2 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 79.9 MB |
| 24 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 1.mp4 53.3 MB |
| 25 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 2.mp4 52.8 MB |
| 26 - Chapter 7. Permutation testing - Comparing means of samples when the population parameters are unknown.mp4 43.7 MB |
| 27 - Chapter 8. Analyzing tables using Pandas.mp4 40.9 MB |
| 28 - Chapter 8. Retrieving table rows.mp4 38.2 MB |
| 29 - Chapter 8. Saving and loading table data.mp4 40.3 MB |
| 3 - Chapter 1. Problem 2 - Analyzing multiple die rolls.mp4 60.9 MB |
| 30 - Chapter 9. Case study 2 solution.mp4 33.6 MB |
| 31 - Chapter 9. Determining statistical significance.mp4 43.6 MB |
| 32 - Case study 3 - Tracking disease outbreaks using news headlines.mp4 6.6 MB |
| 33 - Chapter 10. Clustering data into groups.mp4 61.4 MB |
| 34 - Chapter 10. K-means - A clustering algorithm for grouping data into K central groups.mp4 61.2 MB |
| 35 - Chapter 10. Using density to discover clusters.mp4 52.2 MB |
| 36 - Chapter 10. Clustering based on non-Euclidean distance.mp4 68.8 MB |
| 37 - Chapter 10. Analyzing clusters using Pandas.mp4 40.5 MB |
| 38 - Chapter 11. Geographic location visualization and analysis.mp4 46.6 MB |
| 39 - Chapter 11. Plotting maps using Cartopy.mp4 33.2 MB |
| 4 - Chapter 2. Plotting probabilities using Matplotlib.mp4 53.7 MB |
| 40 - Chapter 11. Visualizing maps.mp4 58.3 MB |
| 41 - Chapter 11. Location tracking using GeoNamesCache.mp4 62.3 MB |
| 42 - Chapter 11. Limitations of the GeoNamesCache library.mp4 69.2 MB |
| 43 - Chapter 12. Case study 3 solution.mp4 34.6 MB |
| 44 - Chapter 12. Visualizing and clustering the extracted location data.mp4 70.7 MB |
| 45 - Case study 4 - Using online job postings to improve your data science resume.mp4 23.9 MB |
| 46 - Chapter 13. Measuring text similarities.mp4 36.3 MB |
| 47 - Chapter 13. Simple text comparison.mp4 44.0 MB |
| 48 - Chapter 13. Replacing words with numeric values.mp4 42.1 MB |
| 49 - Chapter 13. Vectorizing texts using word counts.mp4 44.5 MB |
| 5 - Chapter 2. Comparing multiple coin-flip probability distributions.mp4 65.6 MB |
| 50 - Chapter 13. Using normalization to improve TF vector similarity.mp4 48.6 MB |
| 51 - Chapter 13. Using unit vector dot products to convert between relevance metrics.mp4 41.6 MB |
| 52 - Chapter 13. Basic matrix operations, Part 1.mp4 48.8 MB |
| 53 - Chapter 13. Basic matrix operations, Part 2.mp4 27.1 MB |
| 54 - Chapter 13. Computational limits of matrix multiplication.mp4 47.8 MB |
| 55 - Chapter 14. Dimension reduction of matrix data.mp4 61.7 MB |
| 56 - Chapter 14. Reducing dimensions using rotation, Part 1.mp4 39.0 MB |
| 57 - Chapter 14. Reducing dimensions using rotation, Part 2.mp4 37.6 MB |
| 58 - Chapter 14. Dimension reduction using PCA and scikit-learn.mp4 64.7 MB |
| 59 - Chapter 14. Clustering 4D data in two dimensions.mp4 54.4 MB |
| 6 - Chapter 3. Running random simulations in NumPy.mp4 36.4 MB |
| 60 - Chapter 14. Limitations of PCA.mp4 30.8 MB |
| 61 - Chapter 14. Computing principal components without rotation.mp4 47.8 MB |
| 62 - Chapter 14. Extracting eigenvectors using power iteration, Part 1.mp4 44.7 MB |
| 63 - Chapter 14. Extracting eigenvectors using power iteration, Part 2.mp4 34.4 MB |
| 64 - Chapter 14. Efficient dimension reduction using SVD and scikit-learn.mp4 68.6 MB |
| 65 - Chapter 15. NLP analysis of large text datasets.mp4 47.2 MB |
| 66 - Chapter 15. Vectorizing documents using scikit-learn.mp4 87.1 MB |
| 67 - Chapter 15. Ranking words by both post frequency and count, Part 1.mp4 56.6 MB |
| 68 - Chapter 15. Ranking words by both post frequency and count, Part 2.mp4 48.1 MB |
| 69 - Chapter 15. Computing similarities across large document datasets.mp4 60.2 MB |
| 7 - Chapter 3. Computing confidence intervals using histograms and NumPy arrays.mp4 47.6 MB |
| 70 - Chapter 15. Clustering texts by topic, Part 1.mp4 73.3 MB |
| 71 - Chapter 15. Clustering texts by topic, Part 2.mp4 87.1 MB |
| 72 - Chapter 15. Visualizing text clusters.mp4 58.9 MB |
| 73 - Chapter 15. Using subplots to display multiple word clouds, Part 1.mp4 50.6 MB |
| 74 - Chapter 15. Using subplots to display multiple word clouds, Part 2.mp4 58.8 MB |
| 75 - Chapter 16. Extracting text from web pages.mp4 39.6 MB |
| 76 - Chapter 16. The structure of HTML documents.mp4 62.9 MB |
| 77 - Chapter 16. Parsing HTML using Beautiful Soup, Part 1.mp4 40.4 MB |
| 78 - Chapter 16. Parsing HTML using Beautiful Soup, Part 2.mp4 46.8 MB |
| 79 - Chapter 17. Case study 4 solution.mp4 37.4 MB |
| 8 - Chapter 3. Deriving probabilities from histograms.mp4 57.6 MB |
| 80 - Chapter 17. Exploring the HTML for skill descriptions.mp4 59.7 MB |
| 81 - Chapter 17. Filtering jobs by relevance.mp4 73.2 MB |
| 82 - Chapter 17. Clustering skills in relevant job postings.mp4 66.5 MB |
| 83 - Chapter 17. Investigating the technical skill clusters.mp4 41.5 MB |
| 84 - Chapter 17. Exploring clusters at alternative values of K.mp4 69.4 MB |
| 85 - Chapter 17. Analyzing the 700 most relevant postings.mp4 40.9 MB |
| 86 - Case study 5 - Predicting future friendships from social network data.mp4 80.4 MB |
| 87 - Chapter 18. An introduction to graph theory and network analysis.mp4 74.9 MB |
| 88 - Chapter 18. Analyzing web networks using NetworkX, Part 1.mp4 30.9 MB |
| 89 - Chapter 18. Analyzing web networks using NetworkX, Part 2.mp4 53.1 MB |
| 9 - Chapter 3. Computing histograms in NumPy.mp4 53.0 MB |
| 90 - Chapter 18. Utilizing undirected graphs to optimize the travel time between towns.mp4 57.4 MB |
| 91 - Chapter 18. Computing the fastest travel time between nodes, Part 1.mp4 32.1 MB |
| 92 - Chapter 18. Computing the fastest travel time between nodes, Part 2.mp4 49.0 MB |
| 93 - Chapter 19. Dynamic graph theory techniques for node ranking and social network analysis.mp4 75.1 MB |
| 94 - Chapter 19. Computing travel probabilities using matrix multiplication.mp4 40.2 MB |
| 95 - Chapter 19. Deriving PageRank centrality from probability theory.mp4 48.4 MB |
| 96 - Chapter 19. Computing PageRank centrality using NetworkX.mp4 44.7 MB |
| 97 - Chapter 19. Community detection using Markov clustering, Part 1.mp4 60.1 MB |
| 98 - Chapter 19. Community detection using Markov clustering, Part 2.mp4 75.2 MB |
| 99 - Chapter 19. Uncovering friend groups in social networks.mp4 58.0 MB |
- Torrent indexed: 3 years
- Torrent updated: Wednesday 9th of March 2022 09:30:10 AM
- Torrent hash: 68F1B06F13A8D2DAA4491EE1D44B6731AC249612
- Torrent size: 6.4 GB
- Torrent category: Tutorials
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