Data Science Bookcamp, Video Edition

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Data Science Bookcamp, video edition [TutsNode.com] - Data Science Bookcamp, video edition
  • 113 - Chapter 21. Measuring feature importance with coefficients.mp4 (93.1 MB)
  • 71 - Chapter 15. Clustering texts by topic, Part 2.mp4 (87.1 MB)
  • 66 - Chapter 15. Vectorizing documents using scikit-learn.mp4 (87.1 MB)
  • 86 - Case study 5 - Predicting future friendships from social network data.mp4 (80.4 MB)
  • 23 - Chapter 7. Data dredging - Coming to false conclusions through oversampling.mp4 (79.9 MB)
  • 14 - Chapter 5. Basic probability and statistical analysis using SciPy.mp4 (76.2 MB)
  • 98 - Chapter 19. Community detection using Markov clustering, Part 2.mp4 (75.2 MB)
  • 93 - Chapter 19. Dynamic graph theory techniques for node ranking and social network analysis.mp4 (75.1 MB)
  • 87 - Chapter 18. An introduction to graph theory and network analysis.mp4 (74.9 MB)
  • 16 - Chapter 5. Variance as a measure of dispersion.mp4 (73.9 MB)
  • 70 - Chapter 15. Clustering texts by topic, Part 1.mp4 (73.3 MB)
  • 109 - Chapter 21. Training a linear classifier, Part 2.mp4 (73.3 MB)
  • 81 - Chapter 17. Filtering jobs by relevance.mp4 (73.2 MB)
  • 44 - Chapter 12. Visualizing and clustering the extracted location data.mp4 (70.7 MB)
  • 84 - Chapter 17. Exploring clusters at alternative values of K.mp4 (69.4 MB)
  • 42 - Chapter 11. Limitations of the GeoNamesCache library.mp4 (69.2 MB)
  • 36 - Chapter 10. Clustering based on non-Euclidean distance.mp4 (68.8 MB)
  • 64 - Chapter 14. Efficient dimension reduction using SVD and scikit-learn.mp4 (68.6 MB)
  • 22 - Chapter 7. Assessing the divergence between sample mean and population mean.mp4 (68.3 MB)
  • 82 - Chapter 17. Clustering skills in relevant job postings.mp4 (66.5 MB)
  • 5 - Chapter 2. Comparing multiple coin-flip probability distributions.mp4 (65.6 MB)
  • 114 - Chapter 22. Training nonlinear classifiers with decision tree techniques.mp4 (65.2 MB)
  • 58 - Chapter 14. Dimension reduction using PCA and scikit-learn.mp4 (64.7 MB)
  • 20 - Chapter 6. Computing the area beneath a normal curve.mp4 (64.6 MB)
  • 128 - Chapter 23. Interpreting the trained model.mp4 (64.2 MB)
  • 106 - Chapter 20. Limitations of the KNN algorithm.mp4 (63.2 MB)
  • 76 - Chapter 16. The structure of HTML documents.mp4 (62.9 MB)
  • 41 - Chapter 11. Location tracking using GeoNamesCache.mp4 (62.3 MB)
  • 126 - Chapter 23. Adding profile features to the model.mp4 (62.0 MB)
  • 55 - Chapter 14. Dimension reduction of matrix data.mp4 (61.7 MB)
  • 33 - Chapter 10. Clustering data into groups.mp4 (61.4 MB)
  • 34 - Chapter 10. K-means - A clustering algorithm for grouping data into K central groups.mp4 (61.2 MB)
  • 3 - Chapter 1. Problem 2 - Analyzing multiple die rolls.mp4 (60.9 MB)
  • 69 - Chapter 15. Computing similarities across large document datasets.mp4 (60.2 MB)
  • 97 - Chapter 19. Community detection using Markov clustering, Part 1.mp4 (60.1 MB)
  • 80 - Chapter 17. Exploring the HTML for skill descriptions.mp4 (59.7 MB)
  • 119 - Chapter 22. Studying cancerous cells using feature importance.mp4 (59.3 MB)
  • 72 - Chapter 15. Visualizing text clusters.mp4 (58.9 MB)
  • 74 - Chapter 15. Using subplots to display multiple word clouds, Part 2.mp4 (58.8 MB)
  • 17 - Chapter 6. Making predictions using the central limit theorem and SciPy.mp4 (58.6 MB)
  • 40 - Chapter 11. Visualizing maps.mp4 (58.3 MB)
  • 107 - Chapter 21. Training linear classifiers with logistic regression.mp4 (58.3 MB)
  • 99 - Chapter 19. Uncovering friend groups in social networks.mp4 (58.0 MB)
  • 117 - Chapter 22. Training if_else models with more than two features.mp4 (57.8 MB)
  • 8 - Chapter 3. Deriving probabilities from histograms.mp4 (57.6 MB)
  • 90 - Chapter 18. Utilizing undirected graphs to optimize the travel time between towns.mp4 (57.4 MB)
  • 120 - Chapter 22. Improving performance using random forest classification.mp4 (57.4 MB)
  • 116 - Chapter 22. Deciding which feature to split on.mp4 (57.2 MB)
  • 2 - Chapter 1. Computing probabilities using Python This section covers.mp4 (56.8 MB)
  • 67 - Chapter 15. Ranking words by both post frequency and count, Part 1.mp4 (56.6 MB)
  • 103 - Chapter 20. Measuring predicted label accuracy, Part 2.mp4 (55.2 MB)
  • 19 - Chapter 6. Determining the mean and variance of a population through random sampling.mp4 (55.2 MB)
  • 59 - Chapter 14. Clustering 4D data in two dimensions.mp4 (54.4 MB)
  • 125 - Chapter 23. Training a predictive model using network features, Part 2.mp4 (53.9 MB)
  • 4 - Chapter 2. Plotting probabilities using Matplotlib.mp4 (53.7 MB)
  • 24 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 1.mp4 (53.3 MB)
  • 115 - Chapter 22. Training a nested if_else model using two features.mp4 (53.3 MB)
  • 89 - Chapter 18. Analyzing web networks using NetworkX, Part 2.mp4 (53.1 MB)
  • 9 - Chapter 3. Computing histograms in NumPy.mp4 (53.0 MB)
  • 121 - Chapter 22. Training random forest classifiers using scikit-learn.mp4 (53.0 MB)
  • 25 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 2.mp4 (52.8 MB)
  • 124 - Chapter 23. Training a predictive model using network features, Part 1.mp4 (52.6 MB)
  • 35 - Chapter 10. Using density to discover clusters.mp4 (52.2 MB)
  • 118 - Chapter 22. Training decision tree classifiers using scikit-learn.mp4 (51.9 MB)
  • 73 - Chapter 15. Using subplots to display multiple word clouds, Part 1.mp4 (50.6 MB)
  • 112 - Chapter 21. Training linear classifiers using scikit-learn.mp4 (49.6 MB)
  • 101 - Chapter 20. The basics of supervised machine learning.mp4 (49.2 MB)
  • 92 - Chapter 18. Computing the fastest travel time between nodes, Part 2.mp4 (49.0 MB)
  • 100 - Chapter 20. Network-driven supervised machine learning.mp4 (49.0 MB)
  • 52 - Chapter 13. Basic matrix operations, Part 1.mp4 (48.8 MB)
  • 50 - Chapter 13. Using normalization to improve TF vector similarity.mp4 (48.6 MB)
  • 95 - Chapter 19. Deriving PageRank centrality from probability theory.mp4 (48.4 MB)
  • 68 - Chapter 15. Ranking words by both post frequency and count, Part 2.mp4 (48.1 MB)
  • 54 - Chapter 13. Computational limits of matrix multiplication.mp4 (47.8 MB)
  • 61 - Chapter 14. Computing principal components without rotation.mp4 (47.8 MB)
  • 7 - Chapter 3. Computing confidence intervals using histograms and NumPy arrays.mp4 (47.6 MB)
  • 65 - Chapter 15. NLP analysis of large text datasets.mp4 (47.2 MB)
  • 12 - Chapter 4. Optimizing strategies using the sample space for a 10-card deck.mp4 (47.1 MB)
  • 78 - Chapter 16. Parsing HTML using Beautiful Soup, Part 2.mp4 (46.8 MB)
  • 38 - Chapter 11. Geographic location visualization and analysis.mp4 (46.6 MB)
  • 62 - Chapter 14. Extracting eigenvectors using power iteration, Part 1.mp4 (44.7 MB)

Description


Description
In Video Editions the narrator reads the book while the content, figures, code listings, diagrams, and text appear on the screen. Like an audiobook that you can also watch as a video.

Valuable and accessible… a solid foundation for anyone aspiring to be a data scientist.
Amaresh Rajasekharan, IBM Corporation

Learn data science with Python by building five real-world projects! Experiment with card game predictions, tracking disease outbreaks, and more, as you build a flexible and intuitive understanding of data science.In Data Science Bookcamp you will find:

Techniques for computing and plotting probabilities
Statistical analysis using Scipy
How to organize datasets with clustering algorithms
How to visualize complex multi-variable datasets
How to train a decision tree machine learning algorithm

In Data Science Bookcamp you’ll test and build your knowledge of Python with the kind of open-ended problems that professional data scientists work on every day. Downloadable data sets and thoroughly-explained solutions help you lock in what you’ve learned, building your confidence and making you ready for an exciting new data science career.
about the technology

A data science project has a lot of moving parts, and it takes practice and skill to get all the code, algorithms, datasets, formats, and visualizations working together harmoniously. This unique book guides you through five realistic projects, including tracking disease outbreaks from news headlines, analyzing social networks, and finding relevant patterns in ad click data.

about the book

Data Science Bookcamp doesn’t stop with surface-level theory and toy examples. As you work through each project, you’ll learn how to troubleshoot common problems like missing data, messy data, and algorithms that don’t quite fit the model you’re building. You’ll appreciate the detailed setup instructions and the fully explained solutions that highlight common failure points. In the end, you’ll be confident in your skills because you can see the results.

about the audience

For readers who know the basics of Python. No prior data science or machine learning skills required.

Released 11/2021



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Data Science Bookcamp, Video Edition

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6.5 GB
seeders:33
leechers:19
Data Science Bookcamp, Video Edition

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