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| 001 Case study 1 - Finding the winning strategy in a card game.m4v |
785.75KB |
| 002 Ch1. Computing probabilities using Python This section covers.m4v |
5.62MB |
| 003 Ch1. Problem 2 - Analyzing multiple die rolls.m4v |
6.17MB |
| 004 Ch2. Plotting probabilities using Matplotlib.m4v |
5.76MB |
| 005 Ch2. Comparing multiple coin-flip probability distributions.m4v |
6.27MB |
| 006 Ch3. Running random simulations in NumPy.m4v |
3.71MB |
| 007 Ch3. Computing confidence intervals using histograms and NumPy arrays.m4v |
5.09MB |
| 008 Ch3. Deriving probabilities from histograms.m4v |
5.59MB |
| 009 Ch3. Computing histograms in NumPy.m4v |
5.19MB |
| 010 Ch3. Using permutations to shuffle cards.m4v |
3.59MB |
| 011 Ch4. Case study 1 solution.m4v |
3.68MB |
| 012 Ch4. Optimizing strategies using the sample space for a 10-card deck.m4v |
3.93MB |
| 013 Case study 2 - Assessing online ad clicks for significance.m4v |
2.92MB |
| 014 Ch5. Basic probability and statistical analysis using SciPy.m4v |
6.13MB |
| 015 Ch5. Mean as a measure of centrality.m4v |
4.70MB |
| 016 Ch5. Variance as a measure of dispersion.m4v |
6.72MB |
| 017 Ch6. Making predictions using the central limit theorem and SciPy.m4v |
5.06MB |
| 018 Ch6. Comparing two sampled normal curves.m4v |
3.57MB |
| 019 Ch6. Determining the mean and variance of a population through random sampling.m4v |
5.59MB |
| 020 Ch6. Computing the area beneath a normal curve.m4v |
5.64MB |
| 021 Ch7. Statistical hypothesis testing.m4v |
3.79MB |
| 022 Ch7. Assessing the divergence between sample mean and population mean.m4v |
4.83MB |
| 023 Ch7. Data dredging - Coming to false conclusions through oversampling.m4v |
5.85MB |
| 024 Ch7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 1.m4v |
4.65MB |
| 025 Ch7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 2.m4v |
4.71MB |
| 026 Ch7. Permutation testing - Comparing means of samples when the population parameters are unknown.m4v |
4.14MB |
| 027 Ch8. Analyzing tables using Pandas.m4v |
4.89MB |
| 028 Ch8. Retrieving table rows.m4v |
4.33MB |
| 029 Ch8. Saving and loading table data.m4v |
3.80MB |
| 030 Ch9. Case study 2 solution.m4v |
3.56MB |
| 031 Ch9. Determining statistical significance.m4v |
3.82MB |
| 032 Case study 3 - Tracking disease outbreaks using news headlines.m4v |
772.36KB |
| 033 Ch10. Clustering data into groups.m4v |
5.87MB |
| 034 Ch10. K-means - A clustering algorithm for grouping data into K central groups.m4v |
5.73MB |
| 035 Ch10. Using density to discover clusters.m4v |
4.96MB |
| 036 Ch10. Clustering based on non-Euclidean distance.m4v |
4.87MB |
| 037 Ch10. Analyzing clusters using Pandas.m4v |
3.06MB |
| 038 Ch11. Geographic location visualization and analysis.m4v |
4.49MB |
| 039 Ch11. Plotting maps using Cartopy.m4v |
3.30MB |
| 040 Ch11. Visualizing maps.m4v |
6.38MB |
| 041 Ch11. Location tracking using GeoNamesCache.m4v |
6.02MB |
| 042 Ch11. Limitations of the GeoNamesCache library.m4v |
6.63MB |
| 043 Ch12. Case study 3 solution.m4v |
3.68MB |
| 044 Ch12. Visualizing and clustering the extracted location data.m4v |
6.68MB |
| 045 Case study 4 - Using online job postings to improve your data science resume.m4v |
2.35MB |
| 046 Ch13. Measuring text similarities.m4v |
3.73MB |
| 047 Ch13. Simple text comparison.m4v |
4.82MB |
| 048 Ch13. Replacing words with numeric values.m4v |
4.44MB |
| 049 Ch13. Vectorizing texts using word counts.m4v |
4.67MB |
| 050 Ch13. Using normalization to improve TF vector similarity.m4v |
4.32MB |
| 051 Ch13. Using unit vector dot products to convert between relevance metrics.m4v |
3.99MB |
| 052 Ch13. Basic matrix operations, Part 1.m4v |
5.30MB |
| 053 Ch13. Basic matrix operations, Part 2.m4v |
3.40MB |
| 054 Ch13. Computational limits of matrix multiplication.m4v |
4.47MB |
| 055 Ch14. Dimension reduction of matrix data.m4v |
5.47MB |
| 056 Ch14. Reducing dimensions using rotation, Part 1.m4v |
4.04MB |
| 057 Ch14. Reducing dimensions using rotation, Part 2.m4v |
3.56MB |
| 058 Ch14. Dimension reduction using PCA and scikit-learn.m4v |
6.43MB |
| 059 Ch14. Clustering 4D data in two dimensions.m4v |
4.85MB |
| 060 Ch14. Limitations of PCA.m4v |
3.12MB |
| 061 Ch14. Computing principal components without rotation.m4v |
4.70MB |
| 062 Ch14. Extracting eigenvectors using power iteration, Part 1.m4v |
4.38MB |
| 063 Ch14. Extracting eigenvectors using power iteration, Part 2.m4v |
3.50MB |
| 064 Ch14. Efficient dimension reduction using SVD and scikit-learn.m4v |
5.18MB |
| 065 Ch15. NLP analysis of large text datasets.m4v |
4.49MB |
| 066 Ch15. Vectorizing documents using scikit-learn.m4v |
7.16MB |
| 067 Ch15. Ranking words by both post frequency and count, Part 1.m4v |
4.98MB |
| 068 Ch15. Ranking words by both post frequency and count, Part 2.m4v |
4.57MB |
| 069 Ch15. Computing similarities across large document datasets.m4v |
5.26MB |
| 070 Ch15. Clustering texts by topic, Part 1.m4v |
6.09MB |
| 071 Ch15. Clustering texts by topic, Part 2.m4v |
6.87MB |
| 072 Ch15. Visualizing text clusters.m4v |
5.66MB |
| 073 Ch15. Using subplots to display multiple word clouds, Part 1.m4v |
4.17MB |
| 074 Ch15. Using subplots to display multiple word clouds, Part 2.m4v |
4.37MB |
| 075 Ch16. Extracting text from web pages.m4v |
4.04MB |
| 076 Ch16. The structure of HTML documents.m4v |
5.34MB |
| 077 Ch16. Parsing HTML using Beautiful Soup, Part 1.m4v |
4.44MB |
| 078 Ch16. Parsing HTML using Beautiful Soup, Part 2.m4v |
3.78MB |
| 079 Ch17. Case study 4 solution.m4v |
3.56MB |
| 080 Ch17. Exploring the HTML for skill descriptions.m4v |
4.71MB |
| 081 Ch17. Filtering jobs by relevance.m4v |
7.00MB |
| 082 Ch17. Clustering skills in relevant job postings.m4v |
6.20MB |
| 083 Ch17. Investigating the technical skill clusters.m4v |
4.13MB |
| 084 Ch17. Exploring clusters at alternative values of K.m4v |
5.22MB |
| 085 Ch17. Analyzing the 700 most relevant postings.m4v |
3.73MB |
| 086 Case study 5 - Predicting future friendships from social network data.m4v |
6.84MB |
| 087 Ch18. An introduction to graph theory and network analysis.m4v |
6.05MB |
| 088 Ch18. Analyzing web networks using NetworkX, Part 1.m4v |
3.88MB |
| 089 Ch18. Analyzing web networks using NetworkX, Part 2.m4v |
4.64MB |
| 090 Ch18. Utilizing undirected graphs to optimize the travel time between towns.m4v |
5.65MB |
| 091 Ch18. Computing the fastest travel time between nodes, Part 1.m4v |
3.13MB |
| 092 Ch18. Computing the fastest travel time between nodes, Part 2.m4v |
4.11MB |
| 093 Ch19. Dynamic graph theory techniques for node ranking and social network analysis.m4v |
6.71MB |
| 094 Ch19. Computing travel probabilities using matrix multiplication.m4v |
3.58MB |
| 095 Ch19. Deriving PageRank centrality from probability theory.m4v |
4.29MB |
| 096 Ch19. Computing PageRank centrality using NetworkX.m4v |
3.85MB |
| 097 Ch19. Community detection using Markov clustering, Part 1.m4v |
5.93MB |
| 098 Ch19. Community detection using Markov clustering, Part 2.m4v |
6.74MB |
| 099 Ch19. Uncovering friend groups in social networks.m4v |
4.77MB |
| 100 Ch20. Network-driven supervised machine learning.m4v |
4.33MB |
| 101 Ch20. The basics of supervised machine learning.m4v |
4.29MB |
| 102 Ch20. Measuring predicted label accuracy, Part 1.m4v |
4.74MB |
| 103 Ch20. Measuring predicted label accuracy, Part 2.m4v |
5.44MB |
| 104 Ch20. Optimizing KNN performance.m4v |
3.89MB |
| 105 Ch20. Running a grid search using scikit-learn.m4v |
4.26MB |
| 106 Ch20. Limitations of the KNN algorithm.m4v |
4.88MB |
| 107 Ch21. Training linear classifiers with logistic regression.m4v |
5.63MB |
| 108 Ch21. Training a linear classifier, Part 1.m4v |
4.74MB |
| 109 Ch21. Training a linear classifier, Part 2.m4v |
6.30MB |
| 110 Ch21. Improving linear classification with logistic regression, Part 1.m4v |
4.26MB |
| 111 Ch21. Improving linear classification with logistic regression, Part 2.m4v |
3.88MB |
| 112 Ch21. Training linear classifiers using scikit-learn.m4v |
4.75MB |
| 113 Ch21. Measuring feature importance with coefficients.m4v |
7.38MB |
| 114 Ch22. Training nonlinear classifiers with decision tree techniques.m4v |
6.36MB |
| 115 Ch22. Training a nested if_else model using two features.m4v |
5.34MB |
| 116 Ch22. Deciding which feature to split on.m4v |
5.96MB |
| 117 Ch22. Training if_else models with more than two features.m4v |
5.38MB |
| 118 Ch22. Training decision tree classifiers using scikit-learn.m4v |
4.95MB |
| 119 Ch22. Studying cancerous cells using feature importance.m4v |
5.41MB |
| 120 Ch22. Improving performance using random forest classification.m4v |
5.12MB |
| 121 Ch22. Training random forest classifiers using scikit-learn.m4v |
4.31MB |
| 122 Ch23. Case study 5 solution.m4v |
3.61MB |
| 123 Ch23. Exploring the experimental observations.m4v |
4.09MB |
| 124 Ch23. Training a predictive model using network features, Part 1.m4v |
3.98MB |
| 125 Ch23. Training a predictive model using network features, Part 2.m4v |
4.13MB |
| 126 Ch23. Adding profile features to the model.m4v |
5.21MB |
| 127 Ch23. Optimizing performance across a steady set of features.m4v |
4.03MB |
| 128 Ch23. Interpreting the trained model.m4v |
4.55MB |
| Manning.Data.science.bookcamp.5.real-world.python.projects.2021.pdf |
11.77MB |