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

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[Manning] Data science bookcamp (hevc) (2021) [EN]的二维码

文档列表

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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