TechSEO Boost 2018: Python for SEOs
•
3 gostaram•668 visualizações
In the SEO community, we are fortunate to have many wonderful off-the-shelf SEO tools that cover the most common use cases and problems. But what happens when you want to truly impress your client or boss, or you face challenges nobody has seen or considered before? Do you simply give up, or do you roll up your sleeves to code novel solutions to save the day? In his talk, Hamlet will walk you over some really interesting SEO problems he has successfully solved by using the Python data science stack. You will get access to comprehensive Jupyter notebooks with code you can reuse for your own projects.
Recomendados
Recomendados
Mais conteúdo relacionado
Mais procurados
Mais procurados (20)
Semelhante a TechSEO Boost 2018: Python for SEOs
Semelhante a TechSEO Boost 2018: Python for SEOs (20)
Mais de Catalyst
Mais de Catalyst (20)
Último
Último (20)
TechSEO Boost 2018: Python for SEOs
- 2. Hamlet Batista | @hamletbatista | #TechSEOBoost Python for SEO – Programming As a Superpower
- 3. Hamlet Batista | @hamletbatista | #TechSEOBoost AGENDA – Practical SEO applications of Python => 3.6 for: Data extraction – Preparation – Analysis & Visualization – Machine learning – Deep learning
- 4. Hamlet Batista | @hamletbatista | #TechSEOBoost INTRO – Why program when you can hire a programmer to do the work for you?
- 5. Hamlet Batista | @hamletbatista | #TechSEOBoost But before…
- 6. Hamlet Batista | @hamletbatista | #TechSEOBoost
- 7. Hamlet Batista | @hamletbatista | #TechSEOBoost
- 8. Hamlet Batista | @hamletbatista | #TechSEOBoost
- 9. Hamlet Batista | @hamletbatista | #TechSEOBoost
- 10. Hamlet Batista | @hamletbatista | #TechSEOBoost
- 11. Hamlet Batista | @hamletbatista | #TechSEOBoost
- 12. Hamlet Batista | @hamletbatista | #TechSEOBoost
- 13. Hamlet Batista | @hamletbatista | #TechSEOBoost CHALLENGING SEO PROBLEMS – THAT NEED PROGRAMMING WORK
- 14. Hamlet Batista | @hamletbatista | #TechSEOBoost IBM WebSphere => SAP Hybris
- 15. Hamlet Batista | @hamletbatista | #TechSEOBoost IBM WebSphere Site Category Page (Links to one or more Product Listing Pages) Product Listing Page (Links to one or more Product Pages) Product Page (Single SKU)
- 16. Hamlet Batista | @hamletbatista | #TechSEOBoost SAP Hybris Site Category Page (Links to one or more Product Pages) Product Page (Single SKU)
- 17. Hamlet Batista | @hamletbatista | #TechSEOBoost Old Site Product Pages (717) New Site Product Pages (442) Product Mapping (3431)
- 18. Hamlet Batista | @hamletbatista | #TechSEOBoost Old Site Category Pages (371) New Site Category Pages (147) Category Mapping (712)
- 19. Hamlet Batista | @hamletbatista | #TechSEOBoost
- 20. Hamlet Batista | @hamletbatista | #TechSEOBoost Category Home Product Content Videos Other NewUsersRevenuePageCount
- 21. Hamlet Batista | @hamletbatista | #TechSEOBoost
- 22. Hamlet Batista | @hamletbatista | #TechSEOBoost
- 23. Hamlet Batista | @hamletbatista | #TechSEOBoost Category Home Product Content Videos Other NewUsers
- 24. Hamlet Batista | @hamletbatista | #TechSEOBoost Winners vs Losers
- 25. Hamlet Batista | @hamletbatista | #TechSEOBoost Launch Jupyter Notebook in Google Colaboratory https://colab.research.google.com/github/ranksense/open- source/blob/master/Presentations/TechSEOBoost/2018/PythonforSEOTechSEOBoost2018_ Hamlet_Batista.ipynb
- 26. Hamlet Batista | @hamletbatista | #TechSEOBoost
- 27. Hamlet Batista | @hamletbatista | #TechSEOBoost Ecommerce V3 => Shopify
- 28. Hamlet Batista | @hamletbatista | #TechSEOBoost https://github.com/plotly/plotly.py
- 29. Hamlet Batista | @hamletbatista | #TechSEOBoost Solution Part 1 – Steps Step 1: Pull Google Analytics Data – Step 2: Store Data in Pandas DataFrame – Step 3: Perform Data Preparation and Perform Basic Set Operations CHALLENGE: Find Which Pages Lost SEO Traffic
- 30. Hamlet Batista | @hamletbatista | #TechSEOBoost Python – Basics https://pandas.pydata.org/ Python for Data Science Cheat Sheet https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonF orDataScience.pdf
- 31. Hamlet Batista | @hamletbatista | #TechSEOBoost Python – Jupyter Google Colaboratory https://colab.research.google.com/notebooks/ welcome.ipynb
- 32. Hamlet Batista | @hamletbatista | #TechSEOBoost Python – Pandas https://pandas.pydata.org/ Cheat Sheet https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf 10 Minutes to pandas https://pandas.pydata.org/pandas-docs/stable/10min.html Intro to Pandas for Excel Super Users https://towardsdatascience.com/intro-to-pandas-for-excel- super-users-dac1b38f12b0
- 33. Hamlet Batista | @hamletbatista | #TechSEOBoost Python – Requests WEB SCRAPING REFERENCE: A Simple Cheat Sheet for Web Scraping with Python https://blog.hartleybrody.com/web-scraping-cheat-sheet/ http://docs.python-requests.org/en/master/
- 34. Hamlet Batista | @hamletbatista | #TechSEOBoost https://ga-dev-tools.appspot.com/query-explorer/
- 35. Hamlet Batista | @hamletbatista | #TechSEOBoost Pulling Google Analytics Data
- 36. Hamlet Batista | @hamletbatista | #TechSEOBoost Storing Data in a DataFrame
- 37. Hamlet Batista | @hamletbatista | #TechSEOBoost Transforming Data for Analysis https://www.shanelynn.ie/merge-join-dataframes-python-pandas-index-1/ Left Join Full Outer Join Left Join (if NULL) Inner Join Right Join Right Join (if NULL)
- 38. Hamlet Batista | @hamletbatista | #TechSEOBoost Transforming Data for Analysis
- 39. Hamlet Batista | @hamletbatista | #TechSEOBoost Pages That Lost SEO Traffic
- 40. Hamlet Batista | @hamletbatista | #TechSEOBoost Solution Part 2 – Steps Step 1: We will crawl old pages to follow redirects – Step 2: We will group pages using regular expressions – Step 3: Repeat the previous analysis CHALLENGE: Find Which Page Groups Lost SEO Traffic (Manually)
- 41. Hamlet Batista | @hamletbatista | #TechSEOBoost Regular Expressions for SEOs and Digital Marketers (with Use Cases) https://netpeaksoftware.com/blog/ regular-expressions-for-seos- and-digital-marketers-with-use- cases Regex101.com
- 42. Hamlet Batista | @hamletbatista | #TechSEOBoost Crawling Old Pages
- 43. Hamlet Batista | @hamletbatista | #TechSEOBoost Grouping with Regexes Lookahead and Lookbehind Zero-Length Assertions https://www.regular-expressions.info/lookaround.html
- 44. Hamlet Batista | @hamletbatista | #TechSEOBoost https://github.com/plotly/plotly.py
- 45. Hamlet Batista | @hamletbatista | #TechSEOBoost Page Groups That Lost SEO Traffic
- 46. Hamlet Batista | @hamletbatista | #TechSEOBoost Reverse Engineer Success Too
- 47. Hamlet Batista | @hamletbatista | #TechSEOBoost How Do We Generalize This?
- 48. Hamlet Batista | @hamletbatista | #TechSEOBoost Using Machine Learning!
- 49. Hamlet Batista | @hamletbatista | #TechSEOBoost But before…
- 50. Hamlet Batista | @hamletbatista | #TechSEOBoost Credit: Matt West Why Are Dominicans So Good at Baseball?
- 51. Hamlet Batista | @hamletbatista | #TechSEOBoost Hit a Vitilla? Hit Anything https://www.youtube.com/watch?v=k8Aw2cBer84
- 52. Hamlet Batista | @hamletbatista | #TechSEOBoost Vitilla https://en.wikipedia.org/wiki/Vitilla
- 53. Hamlet Batista | @hamletbatista | #TechSEOBoost Learn Machine Learning and Solve Any SEO Problem
- 54. Hamlet Batista | @hamletbatista | #TechSEOBoost
- 55. Hamlet Batista | @hamletbatista | #TechSEOBoost Regex-> URL Matching XPath-> Content Matching
- 56. Hamlet Batista | @hamletbatista | #TechSEOBoost Solution Part 3 – Steps Step 1: Collect training data – Step 2: Prepare and split training data into training, and testing – Step 3: Find best model CHALLENGE: Find Which Page Groups Lost SEO Traffic (Automatically)
- 57. Hamlet Batista | @hamletbatista | #TechSEOBoost Python – BeautifulSoup BeautifulSoup 4 Cheatsheet http://akul.me/blog/2016/beautifulsoup-cheatsheet/ https://www.crummy.com/software/BeautifulSoup/bs4/download/ An SEO’s guide to XPath https://builtvisible.com/seo-guide-to-xpath/
- 58. Hamlet Batista | @hamletbatista | #TechSEOBoost Python – Scikit-learn https://scikit-learn.org/ Cheat Sheet https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Lear heat_Sheet_Python.pdf Hands-On Introduction To Scikit-learn (sklearn) https://towardsdatascience.com/hands-on-introduction-to-scikit-learn- sklearn-f3df652ff8f2 Efficiently Searching Optimal Tuning Parameters https://www.ritchieng.com/machine-learning-efficiently- search-tuning-param/
- 59. Hamlet Batista | @hamletbatista | #TechSEOBoost Data Scientist Bottom Up Solution Inside the BloomReach Algorithm - Using Machine Learning to Understand Page Templates https://www.bloomreach.com/en/blog/2018/07/using-machine- learning-to-learn-page-templates.html
- 60. Hamlet Batista | @hamletbatista | #TechSEOBoost For most Ecommerce sites, the dimensions and quantity of images and input form elements change by page template. Let’s use that as the features vector. Hamlet’s Observation and Simpler Solution
- 61. Hamlet Batista | @hamletbatista | #TechSEOBoost Hamlet’s Observation and Simpler Solution
- 62. Hamlet Batista | @hamletbatista | #TechSEOBoost Hamlet’s Observation and Simpler Solution
- 63. Hamlet Batista | @hamletbatista | #TechSEOBoost Collecting Training Data
- 64. Hamlet Batista | @hamletbatista | #TechSEOBoost What is One Hot Encoding? Why and when do you have to use it? https://hackernoon.com/what-is-one- hot-encoding-why-and-when-do-you- have-to-use-it-e3c6186d008f Prepare and Split Data
- 65. Hamlet Batista | @hamletbatista | #TechSEOBoost Cross Validation and Grid Search For Model Selection in Python https://stackabuse.com/cross-validation- and-grid-search-for-model-selection-in- python/ Find Best Model
- 66. Hamlet Batista | @hamletbatista | #TechSEOBoost https://github.com/plotly/plotly.py
- 67. Hamlet Batista | @hamletbatista | #TechSEOBoost Simple guide to confusion matrix terminology https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/ Confusion Matrix
- 68. Hamlet Batista | @hamletbatista | #TechSEOBoost But wait… We can do Better
- 69. Hamlet Batista | @hamletbatista | #TechSEOBoost Using Deep Learning!
- 70. Hamlet Batista | @hamletbatista | #TechSEOBoost Solution Part 4 – Steps Step 1: Label a few thousand web page screenshots with the visual features you care about – Step 2: Train a computer vision model to predict more granular page groups – Step 3: Find best model CHALLENGE: Learn More Granular Page Groups that Lost SEO Traffic (Automatically)
- 71. Hamlet Batista | @hamletbatista | #TechSEOBoost https://www.tensorflow.org/ Keras Cheat Sheet https://s3.amazonaws.com/assets.dataca mp.com/blog_assets/Keras_Cheat_Sheet _Python.pdf TensorFlow Tutorial For Beginners https://www.datacamp.com/community/tut orials/tensorflow-tutorial Python – Tensorflow & Keras
- 72. Hamlet Batista | @hamletbatista | #TechSEOBoost Bottleneck The “Information Bottleneck” Theory https://www.quantamagazine.org/ne w-theory-cracks-open-the-black- box-of-deep-learning-20170921/
- 73. Hamlet Batista | @hamletbatista | #TechSEOBoost Encoder Bottleneck Decoder Input Image Reconstructed Image Latent Space Representation AUTOENCODER
- 74. Hamlet Batista | @hamletbatista | #TechSEOBoost 14 x 14 Feature Map 1. Input Image 2. Convolutional Feature Extraction 3. RNN with attention over the image 4. Word by word generation LSTM Encoder Bottleneck Decoder Latent Space Representation Caption Generator
- 75. Hamlet Batista | @hamletbatista | #TechSEOBoost Python – Tensorflow Object Detection API https://github.com/tensorflow/models/tree/master/research/object_detection
- 76. Hamlet Batista | @hamletbatista | #TechSEOBoost AutoML Vision API Tutorial https://cloud.google.com/vision/automl/docs/tutorial Google AutoML
- 77. Hamlet Batista | @hamletbatista | #TechSEOBoost Visually Labeling Screenshots
- 78. Hamlet Batista | @hamletbatista | #TechSEOBoost Don't Take Security Advice from SEO Experts or Psychics https://www.troyhunt.com/dont- take-security-advice-from-seo- experts-or-psychics-neil-patel/
- 79. Hamlet Batista | @hamletbatista | #TechSEOBoost Launch Jupyter Notebook in Google Colaboratory https://colab.research.google.com/github/ranksense/open- source/blob/master/Presentations/TechSEOBoost/2018/Pyt honforSEOTechSEOBoost2018_Hamlet_Batista.ipynb
- 80. Hamlet Batista | @hamletbatista | #TechSEOBoost SUMMARY –
- 81. Hamlet Batista | @hamletbatista | #TechSEOBoost Summary Practical applications of Python => 3.6 for: Data extraction – Preparation – Analysis – Machine learning – Deep learning
- 82. Hamlet Batista | @hamletbatista | #TechSEOBoost Free Realtime SEO Monitor – Ongoing monitoring with no active crawls – Receive alerts about critical SEO issues – Apply quick, temporary fixes in Cloudflare – Create developer tickets for permanent solutions ABOUT RANKSENSE – Apply for Beta Access www.ranksense.com