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[Manning] Data science bookcamp (hevc) (2021) [EN]
magnet:?xt=urn:btih:89f00911282dc5d5b4804682bf1443ce1e98c8b1&dn=[Manning] Data science bookcamp (hevc) (2021) [EN]
磁力链接详情
Hash值:
89f00911282dc5d5b4804682bf1443ce1e98c8b1
点击数:
8
文件大小:
610.43 MB
文件数量:
128
创建日期:
2025-4-20 20:02
最后访问:
2025-4-23 04:38
访问标签:
Manning
Data
science
bookcamp
hevc
2021
EN
文件列表详情
001 Case study 1 - Finding the winning strategy in a card game.m4v 785.75 KB
002 Ch1. Computing probabilities using Python This section covers.m4v 5.62 MB
003 Ch1. Problem 2 - Analyzing multiple die rolls.m4v 6.17 MB
004 Ch2. Plotting probabilities using Matplotlib.m4v 5.76 MB
005 Ch2. Comparing multiple coin-flip probability distributions.m4v 6.27 MB
006 Ch3. Running random simulations in NumPy.m4v 3.71 MB
007 Ch3. Computing confidence intervals using histograms and NumPy arrays.m4v 5.09 MB
008 Ch3. Deriving probabilities from histograms.m4v 5.59 MB
009 Ch3. Computing histograms in NumPy.m4v 5.19 MB
010 Ch3. Using permutations to shuffle cards.m4v 3.59 MB
011 Ch4. Case study 1 solution.m4v 3.68 MB
012 Ch4. Optimizing strategies using the sample space for a 10-card deck.m4v 3.93 MB
013 Case study 2 - Assessing online ad clicks for significance.m4v 2.92 MB
014 Ch5. Basic probability and statistical analysis using SciPy.m4v 6.13 MB
015 Ch5. Mean as a measure of centrality.m4v 4.7 MB
016 Ch5. Variance as a measure of dispersion.m4v 6.72 MB
017 Ch6. Making predictions using the central limit theorem and SciPy.m4v 5.06 MB
018 Ch6. Comparing two sampled normal curves.m4v 3.57 MB
019 Ch6. Determining the mean and variance of a population through random sampling.m4v 5.59 MB
020 Ch6. Computing the area beneath a normal curve.m4v 5.64 MB
021 Ch7. Statistical hypothesis testing.m4v 3.79 MB
022 Ch7. Assessing the divergence between sample mean and population mean.m4v 4.83 MB
023 Ch7. Data dredging - Coming to false conclusions through oversampling.m4v 5.85 MB
024 Ch7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 1.m4v 4.65 MB
025 Ch7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 2.m4v 4.71 MB
026 Ch7. Permutation testing - Comparing means of samples when the population parameters are unknown.m4v 4.14 MB
027 Ch8. Analyzing tables using Pandas.m4v 4.89 MB
028 Ch8. Retrieving table rows.m4v 4.33 MB
029 Ch8. Saving and loading table data.m4v 3.8 MB
030 Ch9. Case study 2 solution.m4v 3.56 MB
031 Ch9. Determining statistical significance.m4v 3.82 MB
032 Case study 3 - Tracking disease outbreaks using news headlines.m4v 772.36 KB
033 Ch10. Clustering data into groups.m4v 5.87 MB
034 Ch10. K-means - A clustering algorithm for grouping data into K central groups.m4v 5.73 MB
035 Ch10. Using density to discover clusters.m4v 4.96 MB
036 Ch10. Clustering based on non-Euclidean distance.m4v 4.87 MB
037 Ch10. Analyzing clusters using Pandas.m4v 3.06 MB
038 Ch11. Geographic location visualization and analysis.m4v 4.49 MB
039 Ch11. Plotting maps using Cartopy.m4v 3.3 MB
040 Ch11. Visualizing maps.m4v 6.38 MB
041 Ch11. Location tracking using GeoNamesCache.m4v 6.02 MB
042 Ch11. Limitations of the GeoNamesCache library.m4v 6.63 MB
043 Ch12. Case study 3 solution.m4v 3.68 MB
044 Ch12. Visualizing and clustering the extracted location data.m4v 6.68 MB
045 Case study 4 - Using online job postings to improve your data science resume.m4v 2.35 MB
046 Ch13. Measuring text similarities.m4v 3.73 MB
047 Ch13. Simple text comparison.m4v 4.82 MB
048 Ch13. Replacing words with numeric values.m4v 4.44 MB
049 Ch13. Vectorizing texts using word counts.m4v 4.67 MB
050 Ch13. Using normalization to improve TF vector similarity.m4v 4.32 MB
051 Ch13. Using unit vector dot products to convert between relevance metrics.m4v 3.99 MB
052 Ch13. Basic matrix operations, Part 1.m4v 5.3 MB
053 Ch13. Basic matrix operations, Part 2.m4v 3.4 MB
054 Ch13. Computational limits of matrix multiplication.m4v 4.47 MB
055 Ch14. Dimension reduction of matrix data.m4v 5.47 MB
056 Ch14. Reducing dimensions using rotation, Part 1.m4v 4.04 MB
057 Ch14. Reducing dimensions using rotation, Part 2.m4v 3.56 MB
058 Ch14. Dimension reduction using PCA and scikit-learn.m4v 6.43 MB
059 Ch14. Clustering 4D data in two dimensions.m4v 4.85 MB
060 Ch14. Limitations of PCA.m4v 3.12 MB
061 Ch14. Computing principal components without rotation.m4v 4.7 MB
062 Ch14. Extracting eigenvectors using power iteration, Part 1.m4v 4.38 MB
063 Ch14. Extracting eigenvectors using power iteration, Part 2.m4v 3.5 MB
064 Ch14. Efficient dimension reduction using SVD and scikit-learn.m4v 5.18 MB
065 Ch15. NLP analysis of large text datasets.m4v 4.49 MB
066 Ch15. Vectorizing documents using scikit-learn.m4v 7.16 MB
067 Ch15. Ranking words by both post frequency and count, Part 1.m4v 4.98 MB
068 Ch15. Ranking words by both post frequency and count, Part 2.m4v 4.57 MB
069 Ch15. Computing similarities across large document datasets.m4v 5.26 MB
070 Ch15. Clustering texts by topic, Part 1.m4v 6.09 MB
071 Ch15. Clustering texts by topic, Part 2.m4v 6.87 MB
072 Ch15. Visualizing text clusters.m4v 5.66 MB
073 Ch15. Using subplots to display multiple word clouds, Part 1.m4v 4.17 MB
074 Ch15. Using subplots to display multiple word clouds, Part 2.m4v 4.37 MB
075 Ch16. Extracting text from web pages.m4v 4.04 MB
076 Ch16. The structure of HTML documents.m4v 5.34 MB
077 Ch16. Parsing HTML using Beautiful Soup, Part 1.m4v 4.44 MB
078 Ch16. Parsing HTML using Beautiful Soup, Part 2.m4v 3.78 MB
079 Ch17. Case study 4 solution.m4v 3.56 MB
080 Ch17. Exploring the HTML for skill descriptions.m4v 4.71 MB
081 Ch17. Filtering jobs by relevance.m4v 7 MB
082 Ch17. Clustering skills in relevant job postings.m4v 6.2 MB
083 Ch17. Investigating the technical skill clusters.m4v 4.13 MB
084 Ch17. Exploring clusters at alternative values of K.m4v 5.22 MB
085 Ch17. Analyzing the 700 most relevant postings.m4v 3.73 MB
086 Case study 5 - Predicting future friendships from social network data.m4v 6.84 MB
087 Ch18. An introduction to graph theory and network analysis.m4v 6.05 MB
088 Ch18. Analyzing web networks using NetworkX, Part 1.m4v 3.88 MB
089 Ch18. Analyzing web networks using NetworkX, Part 2.m4v 4.64 MB
090 Ch18. Utilizing undirected graphs to optimize the travel time between towns.m4v 5.65 MB
091 Ch18. Computing the fastest travel time between nodes, Part 1.m4v 3.13 MB
092 Ch18. Computing the fastest travel time between nodes, Part 2.m4v 4.11 MB
093 Ch19. Dynamic graph theory techniques for node ranking and social network analysis.m4v 6.71 MB
094 Ch19. Computing travel probabilities using matrix multiplication.m4v 3.58 MB
095 Ch19. Deriving PageRank centrality from probability theory.m4v 4.29 MB
096 Ch19. Computing PageRank centrality using NetworkX.m4v 3.85 MB
097 Ch19. Community detection using Markov clustering, Part 1.m4v 5.93 MB
098 Ch19. Community detection using Markov clustering, Part 2.m4v 6.74 MB
099 Ch19. Uncovering friend groups in social networks.m4v 4.77 MB
100 Ch20. Network-driven supervised machine learning.m4v 4.33 MB
101 Ch20. The basics of supervised machine learning.m4v 4.29 MB
102 Ch20. Measuring predicted label accuracy, Part 1.m4v 4.74 MB
103 Ch20. Measuring predicted label accuracy, Part 2.m4v 5.44 MB
104 Ch20. Optimizing KNN performance.m4v 3.89 MB
105 Ch20. Running a grid search using scikit-learn.m4v 4.26 MB
106 Ch20. Limitations of the KNN algorithm.m4v 4.88 MB
107 Ch21. Training linear classifiers with logistic regression.m4v 5.63 MB
108 Ch21. Training a linear classifier, Part 1.m4v 4.74 MB
109 Ch21. Training a linear classifier, Part 2.m4v 6.3 MB
110 Ch21. Improving linear classification with logistic regression, Part 1.m4v 4.26 MB
111 Ch21. Improving linear classification with logistic regression, Part 2.m4v 3.88 MB
112 Ch21. Training linear classifiers using scikit-learn.m4v 4.75 MB
113 Ch21. Measuring feature importance with coefficients.m4v 7.38 MB
114 Ch22. Training nonlinear classifiers with decision tree techniques.m4v 6.36 MB
115 Ch22. Training a nested if_else model using two features.m4v 5.34 MB
116 Ch22. Deciding which feature to split on.m4v 5.96 MB
117 Ch22. Training if_else models with more than two features.m4v 5.38 MB
118 Ch22. Training decision tree classifiers using scikit-learn.m4v 4.95 MB
119 Ch22. Studying cancerous cells using feature importance.m4v 5.41 MB
120 Ch22. Improving performance using random forest classification.m4v 5.12 MB
121 Ch22. Training random forest classifiers using scikit-learn.m4v 4.31 MB
122 Ch23. Case study 5 solution.m4v 3.61 MB
123 Ch23. Exploring the experimental observations.m4v 4.09 MB
124 Ch23. Training a predictive model using network features, Part 1.m4v 3.98 MB
125 Ch23. Training a predictive model using network features, Part 2.m4v 4.13 MB
126 Ch23. Adding profile features to the model.m4v 5.21 MB
127 Ch23. Optimizing performance across a steady set of features.m4v 4.03 MB
128 Ch23. Interpreting the trained model.m4v 4.55 MB
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