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

磁力链接/BT种子名称

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

磁力链接/BT种子简介

种子哈希：89f00911282dc5d5b4804682bf1443ce1e98c8b1
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收录时间：2023-12-22
最近下载：2025-09-20

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文件列表

  • Manning.Data.science.bookcamp.5.real-world.python.projects.2021.pdf 12.3 MB
  • 113 Ch21. Measuring feature importance with coefficients.m4v 7.7 MB
  • 066 Ch15. Vectorizing documents using scikit-learn.m4v 7.5 MB
  • 081 Ch17. Filtering jobs by relevance.m4v 7.3 MB
  • 071 Ch15. Clustering texts by topic, Part 2.m4v 7.2 MB
  • 086 Case study 5 - Predicting future friendships from social network data.m4v 7.2 MB
  • 098 Ch19. Community detection using Markov clustering, Part 2.m4v 7.1 MB
  • 016 Ch5. Variance as a measure of dispersion.m4v 7.0 MB
  • 093 Ch19. Dynamic graph theory techniques for node ranking and social network analysis.m4v 7.0 MB
  • 044 Ch12. Visualizing and clustering the extracted location data.m4v 7.0 MB
  • 042 Ch11. Limitations of the GeoNamesCache library.m4v 7.0 MB
  • 058 Ch14. Dimension reduction using PCA and scikit-learn.m4v 6.7 MB
  • 040 Ch11. Visualizing maps.m4v 6.7 MB
  • 114 Ch22. Training nonlinear classifiers with decision tree techniques.m4v 6.7 MB
  • 109 Ch21. Training a linear classifier, Part 2.m4v 6.6 MB
  • 005 Ch2. Comparing multiple coin-flip probability distributions.m4v 6.6 MB
  • 082 Ch17. Clustering skills in relevant job postings.m4v 6.5 MB
  • 003 Ch1. Problem 2 - Analyzing multiple die rolls.m4v 6.5 MB
  • 014 Ch5. Basic probability and statistical analysis using SciPy.m4v 6.4 MB
  • 070 Ch15. Clustering texts by topic, Part 1.m4v 6.4 MB
  • 087 Ch18. An introduction to graph theory and network analysis.m4v 6.3 MB
  • 041 Ch11. Location tracking using GeoNamesCache.m4v 6.3 MB
  • 116 Ch22. Deciding which feature to split on.m4v 6.2 MB
  • 097 Ch19. Community detection using Markov clustering, Part 1.m4v 6.2 MB
  • 033 Ch10. Clustering data into groups.m4v 6.2 MB
  • 023 Ch7. Data dredging - Coming to false conclusions through oversampling.m4v 6.1 MB
  • 004 Ch2. Plotting probabilities using Matplotlib.m4v 6.0 MB
  • 034 Ch10. K-means - A clustering algorithm for grouping data into K central groups.m4v 6.0 MB
  • 072 Ch15. Visualizing text clusters.m4v 5.9 MB
  • 090 Ch18. Utilizing undirected graphs to optimize the travel time between towns.m4v 5.9 MB
  • 020 Ch6. Computing the area beneath a normal curve.m4v 5.9 MB
  • 107 Ch21. Training linear classifiers with logistic regression.m4v 5.9 MB
  • 002 Ch1. Computing probabilities using Python This section covers.m4v 5.9 MB
  • 019 Ch6. Determining the mean and variance of a population through random sampling.m4v 5.9 MB
  • 008 Ch3. Deriving probabilities from histograms.m4v 5.9 MB
  • 055 Ch14. Dimension reduction of matrix data.m4v 5.7 MB
  • 103 Ch20. Measuring predicted label accuracy, Part 2.m4v 5.7 MB
  • 119 Ch22. Studying cancerous cells using feature importance.m4v 5.7 MB
  • 117 Ch22. Training if_else models with more than two features.m4v 5.6 MB
  • 115 Ch22. Training a nested if_else model using two features.m4v 5.6 MB
  • 076 Ch16. The structure of HTML documents.m4v 5.6 MB
  • 052 Ch13. Basic matrix operations, Part 1.m4v 5.6 MB
  • 069 Ch15. Computing similarities across large document datasets.m4v 5.5 MB
  • 084 Ch17. Exploring clusters at alternative values of K.m4v 5.5 MB
  • 126 Ch23. Adding profile features to the model.m4v 5.5 MB
  • 009 Ch3. Computing histograms in NumPy.m4v 5.4 MB
  • 064 Ch14. Efficient dimension reduction using SVD and scikit-learn.m4v 5.4 MB
  • 120 Ch22. Improving performance using random forest classification.m4v 5.4 MB
  • 007 Ch3. Computing confidence intervals using histograms and NumPy arrays.m4v 5.3 MB
  • 017 Ch6. Making predictions using the central limit theorem and SciPy.m4v 5.3 MB
  • 067 Ch15. Ranking words by both post frequency and count, Part 1.m4v 5.2 MB
  • 035 Ch10. Using density to discover clusters.m4v 5.2 MB
  • 118 Ch22. Training decision tree classifiers using scikit-learn.m4v 5.2 MB
  • 027 Ch8. Analyzing tables using Pandas.m4v 5.1 MB
  • 106 Ch20. Limitations of the KNN algorithm.m4v 5.1 MB
  • 036 Ch10. Clustering based on non-Euclidean distance.m4v 5.1 MB
  • 059 Ch14. Clustering 4D data in two dimensions.m4v 5.1 MB
  • 022 Ch7. Assessing the divergence between sample mean and population mean.m4v 5.1 MB
  • 047 Ch13. Simple text comparison.m4v 5.1 MB
  • 099 Ch19. Uncovering friend groups in social networks.m4v 5.0 MB
  • 112 Ch21. Training linear classifiers using scikit-learn.m4v 5.0 MB
  • 102 Ch20. Measuring predicted label accuracy, Part 1.m4v 5.0 MB
  • 108 Ch21. Training a linear classifier, Part 1.m4v 5.0 MB
  • 080 Ch17. Exploring the HTML for skill descriptions.m4v 4.9 MB
  • 025 Ch7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 2.m4v 4.9 MB
  • 015 Ch5. Mean as a measure of centrality.m4v 4.9 MB
  • 061 Ch14. Computing principal components without rotation.m4v 4.9 MB
  • 049 Ch13. Vectorizing texts using word counts.m4v 4.9 MB
  • 024 Ch7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 1.m4v 4.9 MB
  • 089 Ch18. Analyzing web networks using NetworkX, Part 2.m4v 4.9 MB
  • 068 Ch15. Ranking words by both post frequency and count, Part 2.m4v 4.8 MB
  • 128 Ch23. Interpreting the trained model.m4v 4.8 MB
  • 065 Ch15. NLP analysis of large text datasets.m4v 4.7 MB
  • 038 Ch11. Geographic location visualization and analysis.m4v 4.7 MB
  • 054 Ch13. Computational limits of matrix multiplication.m4v 4.7 MB
  • 077 Ch16. Parsing HTML using Beautiful Soup, Part 1.m4v 4.7 MB
  • 048 Ch13. Replacing words with numeric values.m4v 4.7 MB
  • 062 Ch14. Extracting eigenvectors using power iteration, Part 1.m4v 4.6 MB
  • 074 Ch15. Using subplots to display multiple word clouds, Part 2.m4v 4.6 MB
  • 100 Ch20. Network-driven supervised machine learning.m4v 4.5 MB
  • 028 Ch8. Retrieving table rows.m4v 4.5 MB
  • 050 Ch13. Using normalization to improve TF vector similarity.m4v 4.5 MB
  • 121 Ch22. Training random forest classifiers using scikit-learn.m4v 4.5 MB
  • 095 Ch19. Deriving PageRank centrality from probability theory.m4v 4.5 MB
  • 101 Ch20. The basics of supervised machine learning.m4v 4.5 MB
  • 110 Ch21. Improving linear classification with logistic regression, Part 1.m4v 4.5 MB
  • 105 Ch20. Running a grid search using scikit-learn.m4v 4.5 MB
  • 073 Ch15. Using subplots to display multiple word clouds, Part 1.m4v 4.4 MB
  • 026 Ch7. Permutation testing - Comparing means of samples when the population parameters are unknown.m4v 4.3 MB
  • 125 Ch23. Training a predictive model using network features, Part 2.m4v 4.3 MB
  • 083 Ch17. Investigating the technical skill clusters.m4v 4.3 MB
  • 092 Ch18. Computing the fastest travel time between nodes, Part 2.m4v 4.3 MB
  • 123 Ch23. Exploring the experimental observations.m4v 4.3 MB
  • 075 Ch16. Extracting text from web pages.m4v 4.2 MB
  • 056 Ch14. Reducing dimensions using rotation, Part 1.m4v 4.2 MB
  • 127 Ch23. Optimizing performance across a steady set of features.m4v 4.2 MB
  • 051 Ch13. Using unit vector dot products to convert between relevance metrics.m4v 4.2 MB
  • 124 Ch23. Training a predictive model using network features, Part 1.m4v 4.2 MB
  • 012 Ch4. Optimizing strategies using the sample space for a 10-card deck.m4v 4.1 MB
  • 104 Ch20. Optimizing KNN performance.m4v 4.1 MB
  • 088 Ch18. Analyzing web networks using NetworkX, Part 1.m4v 4.1 MB
  • 111 Ch21. Improving linear classification with logistic regression, Part 2.m4v 4.1 MB
  • 096 Ch19. Computing PageRank centrality using NetworkX.m4v 4.0 MB
  • 031 Ch9. Determining statistical significance.m4v 4.0 MB
  • 029 Ch8. Saving and loading table data.m4v 4.0 MB
  • 021 Ch7. Statistical hypothesis testing.m4v 4.0 MB
  • 078 Ch16. Parsing HTML using Beautiful Soup, Part 2.m4v 4.0 MB
  • 046 Ch13. Measuring text similarities.m4v 3.9 MB
  • 085 Ch17. Analyzing the 700 most relevant postings.m4v 3.9 MB
  • 006 Ch3. Running random simulations in NumPy.m4v 3.9 MB
  • 043 Ch12. Case study 3 solution.m4v 3.9 MB
  • 011 Ch4. Case study 1 solution.m4v 3.9 MB
  • 122 Ch23. Case study 5 solution.m4v 3.8 MB
  • 010 Ch3. Using permutations to shuffle cards.m4v 3.8 MB
  • 094 Ch19. Computing travel probabilities using matrix multiplication.m4v 3.8 MB
  • 018 Ch6. Comparing two sampled normal curves.m4v 3.7 MB
  • 057 Ch14. Reducing dimensions using rotation, Part 2.m4v 3.7 MB
  • 079 Ch17. Case study 4 solution.m4v 3.7 MB
  • 030 Ch9. Case study 2 solution.m4v 3.7 MB
  • 063 Ch14. Extracting eigenvectors using power iteration, Part 2.m4v 3.7 MB
  • 053 Ch13. Basic matrix operations, Part 2.m4v 3.6 MB
  • 039 Ch11. Plotting maps using Cartopy.m4v 3.5 MB
  • 091 Ch18. Computing the fastest travel time between nodes, Part 1.m4v 3.3 MB
  • 060 Ch14. Limitations of PCA.m4v 3.3 MB
  • 037 Ch10. Analyzing clusters using Pandas.m4v 3.2 MB
  • 013 Case study 2 - Assessing online ad clicks for significance.m4v 3.1 MB
  • 045 Case study 4 - Using online job postings to improve your data science resume.m4v 2.5 MB
  • 001 Case study 1 - Finding the winning strategy in a card game.m4v 804.6 kB
  • 032 Case study 3 - Tracking disease outbreaks using news headlines.m4v 790.9 kB

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