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种子名称： [Manning] Data science bookcamp (hevc) (2021) [EN]
文件类型： 视频
文件数目： 128个文件
文件大小： 610.43 MB
收录时间： 2025-4-20 20:02
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资源热度： 16
最近下载： 2025-4-28 22:43

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[Manning] Data science bookcamp (hevc) (2021) [EN].torrent

001 Case study 1 - Finding the winning strategy in a card game.m4v785.75KB

002 Ch1. Computing probabilities using Python This section covers.m4v5.62MB

003 Ch1. Problem 2 - Analyzing multiple die rolls.m4v6.17MB

004 Ch2. Plotting probabilities using Matplotlib.m4v5.76MB

005 Ch2. Comparing multiple coin-flip probability distributions.m4v6.27MB

006 Ch3. Running random simulations in NumPy.m4v3.71MB

007 Ch3. Computing confidence intervals using histograms and NumPy arrays.m4v5.09MB

008 Ch3. Deriving probabilities from histograms.m4v5.59MB

009 Ch3. Computing histograms in NumPy.m4v5.19MB

010 Ch3. Using permutations to shuffle cards.m4v3.59MB

011 Ch4. Case study 1 solution.m4v3.68MB

012 Ch4. Optimizing strategies using the sample space for a 10-card deck.m4v3.93MB

013 Case study 2 - Assessing online ad clicks for significance.m4v2.92MB

014 Ch5. Basic probability and statistical analysis using SciPy.m4v6.13MB

015 Ch5. Mean as a measure of centrality.m4v4.7MB

016 Ch5. Variance as a measure of dispersion.m4v6.72MB

017 Ch6. Making predictions using the central limit theorem and SciPy.m4v5.06MB

018 Ch6. Comparing two sampled normal curves.m4v3.57MB

019 Ch6. Determining the mean and variance of a population through random sampling.m4v5.59MB

020 Ch6. Computing the area beneath a normal curve.m4v5.64MB

021 Ch7. Statistical hypothesis testing.m4v3.79MB

022 Ch7. Assessing the divergence between sample mean and population mean.m4v4.83MB

023 Ch7. Data dredging - Coming to false conclusions through oversampling.m4v5.85MB

024 Ch7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 1.m4v4.65MB

025 Ch7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 2.m4v4.71MB

026 Ch7. Permutation testing - Comparing means of samples when the population parameters are unknown.m4v4.14MB

027 Ch8. Analyzing tables using Pandas.m4v4.89MB

028 Ch8. Retrieving table rows.m4v4.33MB

029 Ch8. Saving and loading table data.m4v3.8MB

030 Ch9. Case study 2 solution.m4v3.56MB

031 Ch9. Determining statistical significance.m4v3.82MB

032 Case study 3 - Tracking disease outbreaks using news headlines.m4v772.36KB

033 Ch10. Clustering data into groups.m4v5.87MB

034 Ch10. K-means - A clustering algorithm for grouping data into K central groups.m4v5.73MB

035 Ch10. Using density to discover clusters.m4v4.96MB

036 Ch10. Clustering based on non-Euclidean distance.m4v4.87MB

037 Ch10. Analyzing clusters using Pandas.m4v3.06MB

038 Ch11. Geographic location visualization and analysis.m4v4.49MB

039 Ch11. Plotting maps using Cartopy.m4v3.3MB

040 Ch11. Visualizing maps.m4v6.38MB

041 Ch11. Location tracking using GeoNamesCache.m4v6.02MB

042 Ch11. Limitations of the GeoNamesCache library.m4v6.63MB

043 Ch12. Case study 3 solution.m4v3.68MB

044 Ch12. Visualizing and clustering the extracted location data.m4v6.68MB

045 Case study 4 - Using online job postings to improve your data science resume.m4v2.35MB

046 Ch13. Measuring text similarities.m4v3.73MB

047 Ch13. Simple text comparison.m4v4.82MB

048 Ch13. Replacing words with numeric values.m4v4.44MB

049 Ch13. Vectorizing texts using word counts.m4v4.67MB

050 Ch13. Using normalization to improve TF vector similarity.m4v4.32MB

051 Ch13. Using unit vector dot products to convert between relevance metrics.m4v3.99MB

052 Ch13. Basic matrix operations, Part 1.m4v5.3MB

053 Ch13. Basic matrix operations, Part 2.m4v3.4MB

054 Ch13. Computational limits of matrix multiplication.m4v4.47MB

055 Ch14. Dimension reduction of matrix data.m4v5.47MB

056 Ch14. Reducing dimensions using rotation, Part 1.m4v4.04MB

057 Ch14. Reducing dimensions using rotation, Part 2.m4v3.56MB

058 Ch14. Dimension reduction using PCA and scikit-learn.m4v6.43MB

059 Ch14. Clustering 4D data in two dimensions.m4v4.85MB

060 Ch14. Limitations of PCA.m4v3.12MB

061 Ch14. Computing principal components without rotation.m4v4.7MB

062 Ch14. Extracting eigenvectors using power iteration, Part 1.m4v4.38MB

063 Ch14. Extracting eigenvectors using power iteration, Part 2.m4v3.5MB

064 Ch14. Efficient dimension reduction using SVD and scikit-learn.m4v5.18MB

065 Ch15. NLP analysis of large text datasets.m4v4.49MB

066 Ch15. Vectorizing documents using scikit-learn.m4v7.16MB

067 Ch15. Ranking words by both post frequency and count, Part 1.m4v4.98MB

068 Ch15. Ranking words by both post frequency and count, Part 2.m4v4.57MB

069 Ch15. Computing similarities across large document datasets.m4v5.26MB

070 Ch15. Clustering texts by topic, Part 1.m4v6.09MB

071 Ch15. Clustering texts by topic, Part 2.m4v6.87MB

072 Ch15. Visualizing text clusters.m4v5.66MB

073 Ch15. Using subplots to display multiple word clouds, Part 1.m4v4.17MB

074 Ch15. Using subplots to display multiple word clouds, Part 2.m4v4.37MB

075 Ch16. Extracting text from web pages.m4v4.04MB

076 Ch16. The structure of HTML documents.m4v5.34MB

077 Ch16. Parsing HTML using Beautiful Soup, Part 1.m4v4.44MB

078 Ch16. Parsing HTML using Beautiful Soup, Part 2.m4v3.78MB

079 Ch17. Case study 4 solution.m4v3.56MB

080 Ch17. Exploring the HTML for skill descriptions.m4v4.71MB

081 Ch17. Filtering jobs by relevance.m4v7MB

082 Ch17. Clustering skills in relevant job postings.m4v6.2MB

083 Ch17. Investigating the technical skill clusters.m4v4.13MB

084 Ch17. Exploring clusters at alternative values of K.m4v5.22MB

085 Ch17. Analyzing the 700 most relevant postings.m4v3.73MB

086 Case study 5 - Predicting future friendships from social network data.m4v6.84MB

087 Ch18. An introduction to graph theory and network analysis.m4v6.05MB

088 Ch18. Analyzing web networks using NetworkX, Part 1.m4v3.88MB

089 Ch18. Analyzing web networks using NetworkX, Part 2.m4v4.64MB

090 Ch18. Utilizing undirected graphs to optimize the travel time between towns.m4v5.65MB

091 Ch18. Computing the fastest travel time between nodes, Part 1.m4v3.13MB

092 Ch18. Computing the fastest travel time between nodes, Part 2.m4v4.11MB

093 Ch19. Dynamic graph theory techniques for node ranking and social network analysis.m4v6.71MB

094 Ch19. Computing travel probabilities using matrix multiplication.m4v3.58MB

095 Ch19. Deriving PageRank centrality from probability theory.m4v4.29MB

096 Ch19. Computing PageRank centrality using NetworkX.m4v3.85MB

097 Ch19. Community detection using Markov clustering, Part 1.m4v5.93MB

098 Ch19. Community detection using Markov clustering, Part 2.m4v6.74MB

099 Ch19. Uncovering friend groups in social networks.m4v4.77MB

100 Ch20. Network-driven supervised machine learning.m4v4.33MB

101 Ch20. The basics of supervised machine learning.m4v4.29MB

102 Ch20. Measuring predicted label accuracy, Part 1.m4v4.74MB

103 Ch20. Measuring predicted label accuracy, Part 2.m4v5.44MB

104 Ch20. Optimizing KNN performance.m4v3.89MB

105 Ch20. Running a grid search using scikit-learn.m4v4.26MB

106 Ch20. Limitations of the KNN algorithm.m4v4.88MB

107 Ch21. Training linear classifiers with logistic regression.m4v5.63MB

108 Ch21. Training a linear classifier, Part 1.m4v4.74MB

109 Ch21. Training a linear classifier, Part 2.m4v6.3MB

110 Ch21. Improving linear classification with logistic regression, Part 1.m4v4.26MB

111 Ch21. Improving linear classification with logistic regression, Part 2.m4v3.88MB

112 Ch21. Training linear classifiers using scikit-learn.m4v4.75MB

113 Ch21. Measuring feature importance with coefficients.m4v7.38MB

114 Ch22. Training nonlinear classifiers with decision tree techniques.m4v6.36MB

115 Ch22. Training a nested if_else model using two features.m4v5.34MB

116 Ch22. Deciding which feature to split on.m4v5.96MB

117 Ch22. Training if_else models with more than two features.m4v5.38MB

118 Ch22. Training decision tree classifiers using scikit-learn.m4v4.95MB

119 Ch22. Studying cancerous cells using feature importance.m4v5.41MB

120 Ch22. Improving performance using random forest classification.m4v5.12MB

121 Ch22. Training random forest classifiers using scikit-learn.m4v4.31MB

122 Ch23. Case study 5 solution.m4v3.61MB

123 Ch23. Exploring the experimental observations.m4v4.09MB

124 Ch23. Training a predictive model using network features, Part 1.m4v3.98MB

125 Ch23. Training a predictive model using network features, Part 2.m4v4.13MB

126 Ch23. Adding profile features to the model.m4v5.21MB

127 Ch23. Optimizing performance across a steady set of features.m4v4.03MB

128 Ch23. Interpreting the trained model.m4v4.55MB