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O`REILLY - Data Science Bookcamp, VIDEO EDITION

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种子名称: O`REILLY - Data Science Bookcamp, VIDEO EDITION
文件类型: 视频
文件数目: 128个文件
文件大小: 6.44 GB
收录时间: 2022-11-22 17:30
已经下载: 3
资源热度: 245
最近下载: 2024-12-20 22:31

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O`REILLY - Data Science Bookcamp, VIDEO EDITION.torrent
  • 01 - Case study 1 - Finding the winning strategy in a card game.mp46.89MB
  • 02 - Chapter 1. Computing probabilities using Python This section covers.mp456.75MB
  • 03 - Chapter 1. Problem 2 - Analyzing multiple die rolls.mp460.89MB
  • 04 - Chapter 2. Plotting probabilities using Matplotlib.mp453.74MB
  • 05 - Chapter 2. Comparing multiple coin-flip probability distributions.mp465.57MB
  • 06 - Chapter 3. Running random simulations in NumPy.mp436.35MB
  • 07 - Chapter 3. Computing confidence intervals using histograms and NumPy arrays.mp447.59MB
  • 08 - Chapter 3. Deriving probabilities from histograms.mp457.63MB
  • 09 - Chapter 3. Computing histograms in NumPy.mp452.99MB
  • 10 - Chapter 3. Using permutations to shuffle cards.mp435.4MB
  • 11 - Chapter 4. Case study 1 solution.mp434.27MB
  • 12 - Chapter 4. Optimizing strategies using the sample space for a 10-card deck.mp447.1MB
  • 13 - Case study 2 - Assessing online ad clicks for significance.mp431.4MB
  • 14 - Chapter 5. Basic probability and statistical analysis using SciPy.mp476.23MB
  • 15 - Chapter 5. Mean as a measure of centrality.mp436.58MB
  • 16 - Chapter 5. Variance as a measure of dispersion.mp473.89MB
  • 17 - Chapter 6. Making predictions using the central limit theorem and SciPy.mp458.61MB
  • 18 - Chapter 6. Comparing two sampled normal curves.mp431.46MB
  • 19 - Chapter 6. Determining the mean and variance of a population through random sampling.mp455.19MB
  • 20 - Chapter 6. Computing the area beneath a normal curve.mp464.57MB
  • 21 - Chapter 7. Statistical hypothesis testing.mp439.19MB
  • 22 - Chapter 7. Assessing the divergence between sample mean and population mean.mp468.3MB
  • 23 - Chapter 7. Data dredging - Coming to false conclusions through oversampling.mp479.88MB
  • 24 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 1.mp453.28MB
  • 25 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 2.mp452.78MB
  • 26 - Chapter 7. Permutation testing - Comparing means of samples when the population parameters are unknown.mp443.69MB
  • 27 - Chapter 8. Analyzing tables using Pandas.mp440.87MB
  • 28 - Chapter 8. Retrieving table rows.mp438.24MB
  • 29 - Chapter 8. Saving and loading table data.mp440.28MB
  • 30 - Chapter 9. Case study 2 solution.mp433.6MB
  • 31 - Chapter 9. Determining statistical significance.mp443.58MB
  • 32 - Case study 3 - Tracking disease outbreaks using news headlines.mp46.6MB
  • 33 - Chapter 10. Clustering data into groups.mp461.4MB
  • 34 - Chapter 10. K-means - A clustering algorithm for grouping data into K central groups.mp461.2MB
  • 35 - Chapter 10. Using density to discover clusters.mp452.23MB
  • 36 - Chapter 10. Clustering based on non-Euclidean distance.mp468.79MB
  • 37 - Chapter 10. Analyzing clusters using Pandas.mp440.48MB
  • 38 - Chapter 11. Geographic location visualization and analysis.mp446.58MB
  • 39 - Chapter 11. Plotting maps using Cartopy.mp433.23MB
  • 40 - Chapter 11. Visualizing maps.mp458.27MB
  • 41 - Chapter 11. Location tracking using GeoNamesCache.mp462.35MB
  • 42 - Chapter 11. Limitations of the GeoNamesCache library.mp469.19MB
  • 43 - Chapter 12. Case study 3 solution.mp434.63MB
  • 44 - Chapter 12. Visualizing and clustering the extracted location data.mp470.72MB
  • 45 - Case study 4 - Using online job postings to improve your data science resume.mp423.95MB
  • 46 - Chapter 13. Measuring text similarities.mp436.28MB
  • 47 - Chapter 13. Simple text comparison.mp444MB
  • 48 - Chapter 13. Replacing words with numeric values.mp442.07MB
  • 49 - Chapter 13. Vectorizing texts using word counts.mp444.5MB
  • 50 - Chapter 13. Using normalization to improve TF vector similarity.mp448.56MB
  • 51 - Chapter 13. Using unit vector dot products to convert between relevance metrics.mp441.64MB
  • 52 - Chapter 13. Basic matrix operations, Part 1.mp448.78MB
  • 53 - Chapter 13. Basic matrix operations, Part 2.mp427.15MB
  • 54 - Chapter 13. Computational limits of matrix multiplication.mp447.81MB
  • 55 - Chapter 14. Dimension reduction of matrix data.mp461.74MB
  • 56 - Chapter 14. Reducing dimensions using rotation, Part 1.mp438.99MB
  • 57 - Chapter 14. Reducing dimensions using rotation, Part 2.mp437.56MB
  • 58 - Chapter 14. Dimension reduction using PCA and scikit-learn.mp464.72MB
  • 59 - Chapter 14. Clustering 4D data in two dimensions.mp454.44MB
  • 60 - Chapter 14. Limitations of PCA.mp430.77MB
  • 61 - Chapter 14. Computing principal components without rotation.mp447.8MB
  • 62 - Chapter 14. Extracting eigenvectors using power iteration, Part 1.mp444.67MB
  • 63 - Chapter 14. Extracting eigenvectors using power iteration, Part 2.mp434.38MB
  • 64 - Chapter 14. Efficient dimension reduction using SVD and scikit-learn.mp468.6MB
  • 65 - Chapter 15. NLP analysis of large text datasets.mp447.16MB
  • 66 - Chapter 15. Vectorizing documents using scikit-learn.mp487.06MB
  • 67 - Chapter 15. Ranking words by both post frequency and count, Part 1.mp456.59MB
  • 68 - Chapter 15. Ranking words by both post frequency and count, Part 2.mp448.13MB
  • 69 - Chapter 15. Computing similarities across large document datasets.mp460.24MB
  • 70 - Chapter 15. Clustering texts by topic, Part 1.mp473.3MB
  • 71 - Chapter 15. Clustering texts by topic, Part 2.mp487.08MB
  • 72 - Chapter 15. Visualizing text clusters.mp458.9MB
  • 73 - Chapter 15. Using subplots to display multiple word clouds, Part 1.mp450.57MB
  • 74 - Chapter 15. Using subplots to display multiple word clouds, Part 2.mp458.83MB
  • 75 - Chapter 16. Extracting text from web pages.mp439.55MB
  • 76 - Chapter 16. The structure of HTML documents.mp462.95MB
  • 77 - Chapter 16. Parsing HTML using Beautiful Soup, Part 1.mp440.42MB
  • 78 - Chapter 16. Parsing HTML using Beautiful Soup, Part 2.mp446.78MB
  • 79 - Chapter 17. Case study 4 solution.mp437.42MB
  • 80 - Chapter 17. Exploring the HTML for skill descriptions.mp459.65MB
  • 81 - Chapter 17. Filtering jobs by relevance.mp473.18MB
  • 82 - Chapter 17. Clustering skills in relevant job postings.mp466.54MB
  • 83 - Chapter 17. Investigating the technical skill clusters.mp441.46MB
  • 84 - Chapter 17. Exploring clusters at alternative values of K.mp469.37MB
  • 85 - Chapter 17. Analyzing the 700 most relevant postings.mp440.95MB
  • 86 - Case study 5 - Predicting future friendships from social network data.mp480.4MB
  • 87 - Chapter 18. An introduction to graph theory and network analysis.mp474.88MB
  • 88 - Chapter 18. Analyzing web networks using NetworkX, Part 1.mp430.92MB
  • 89 - Chapter 18. Analyzing web networks using NetworkX, Part 2.mp453.06MB
  • 90 - Chapter 18. Utilizing undirected graphs to optimize the travel time between towns.mp457.39MB
  • 91 - Chapter 18. Computing the fastest travel time between nodes, Part 1.mp432.12MB
  • 92 - Chapter 18. Computing the fastest travel time between nodes, Part 2.mp449.04MB
  • 93 - Chapter 19. Dynamic graph theory techniques for node ranking and social network analysis.mp475.08MB
  • 94 - Chapter 19. Computing travel probabilities using matrix multiplication.mp440.21MB
  • 95 - Chapter 19. Deriving PageRank centrality from probability theory.mp448.36MB
  • 96 - Chapter 19. Computing PageRank centrality using NetworkX.mp444.66MB
  • 97 - Chapter 19. Community detection using Markov clustering, Part 1.mp460.05MB
  • 98 - Chapter 19. Community detection using Markov clustering, Part 2.mp475.21MB
  • 99 - Chapter 19. Uncovering friend groups in social networks.mp457.99MB
  • 100 - Chapter 20. Network-driven supervised machine learning.mp448.95MB
  • 101 - Chapter 20. The basics of supervised machine learning.mp449.2MB
  • 102 - Chapter 20. Measuring predicted label accuracy, Part 1.mp437.28MB
  • 103 - Chapter 20. Measuring predicted label accuracy, Part 2.mp455.24MB
  • 104 - Chapter 20. Optimizing KNN performance.mp435.68MB
  • 105 - Chapter 20. Running a grid search using scikit-learn.mp439.33MB
  • 106 - Chapter 20. Limitations of the KNN algorithm.mp463.16MB
  • 107 - Chapter 21. Training linear classifiers with logistic regression.mp458.26MB
  • 108 - Chapter 21. Training a linear classifier, Part 1.mp443.52MB
  • 109 - Chapter 21. Training a linear classifier, Part 2.mp473.26MB
  • 110 - Chapter 21. Improving linear classification with logistic regression, Part 1.mp443.42MB
  • 111 - Chapter 21. Improving linear classification with logistic regression, Part 2.mp443.12MB
  • 112 - Chapter 21. Training linear classifiers using scikit-learn.mp449.64MB
  • 113 - Chapter 21. Measuring feature importance with coefficients.mp493.13MB
  • 114 - Chapter 22. Training nonlinear classifiers with decision tree techniques.mp465.2MB
  • 115 - Chapter 22. Training a nested if_else model using two features.mp453.25MB
  • 116 - Chapter 22. Deciding which feature to split on.mp457.23MB
  • 117 - Chapter 22. Training if_else models with more than two features.mp457.79MB
  • 118 - Chapter 22. Training decision tree classifiers using scikit-learn.mp451.86MB
  • 119 - Chapter 22. Studying cancerous cells using feature importance.mp459.29MB
  • 120 - Chapter 22. Improving performance using random forest classification.mp457.38MB
  • 121 - Chapter 22. Training random forest classifiers using scikit-learn.mp452.96MB
  • 122 - Chapter 23. Case study 5 solution.mp432.94MB
  • 123 - Chapter 23. Exploring the experimental observations.mp438.99MB
  • 124 - Chapter 23. Training a predictive model using network features, Part 1.mp452.59MB
  • 125 - Chapter 23. Training a predictive model using network features, Part 2.mp453.87MB
  • 126 - Chapter 23. Adding profile features to the model.mp462.03MB
  • 127 - Chapter 23. Optimizing performance across a steady set of features.mp442.55MB
  • 128 - Chapter 23. Interpreting the trained model.mp464.17MB