| 1. Course content.mp4 | 49.1 MB | ||
| 1. Course content.srt | 7.7 KB | ||
| 1. Final remarks.mp4 | 7.3 MB | ||
| 1. Final remarks.srt | 1.4 KB | ||
| 1. Plan of attack.mp4 | 16 MB | ||
| 1. Plan of attack.srt | 3.2 KB | ||
| 1.1 Source code - Google Colab.html | 102.4 B | ||
| 10. Clustering similar positions 1.mp4 | 54.2 MB | ||
| 10. Clustering similar positions 1.srt | 8.4 KB | ||
| 10. Messages dataset.mp4 | 28.3 MB | ||
| 10. Messages dataset.srt | 4.9 KB | ||
| 11. Clustering similar positions 2.mp4 | 53.8 MB | ||
| 11. Clustering similar positions 2.srt | 8.1 KB | ||
| 12. Clustering similar positions 3.mp4 | 59.6 MB | ||
| 12. Clustering similar positions 3.srt | 8.2 KB | ||
| 13. Clustering similar positions 4.mp4 | 49.6 MB | ||
| 13. Clustering similar positions 4.srt | 6.3 KB | ||
| 14. Visualizing the clusters.mp4 | 103.8 MB | ||
| 14. Visualizing the clusters.srt | 11.3 KB | ||
| 15. Exporting to JSON.mp4 | 41.2 MB | ||
| 15. Exporting to JSON.srt | 6.4 KB | ||
| 16. Visualizing using dendrogram.mp4 | 54.3 MB | ||
| 16. Visualizing using dendrogram.srt | 6.2 KB | ||
| 17. Visualizing using link tree.mp4 | 17.4 MB | ||
| 17. Visualizing using link tree.srt | 1.9 KB | ||
| 18. Google location API.mp4 | 48.2 MB | ||
| 18. Google location API.srt | 9.3 KB | ||
| 19. Using the location API.mp4 | 34.2 MB | ||
| 19. Using the location API.srt | 4.4 KB | ||
| 2. Connections by day.mp4 | 81.1 MB | ||
| 2. Connections by day.srt | 11.2 KB | ||
| 2. Course materials.html | 307.2 B | ||
| 2. Creating a LinkedIn APP.mp4 | 24.9 MB | ||
| 2. Creating a LinkedIn APP.srt | 5.8 KB | ||
| 2. Loading the dataset.mp4 | 16 MB | ||
| 2. Loading the dataset.srt | 3 KB | ||
| 20. Latitude and longitude of the contacts.mp4 | 31.5 MB | ||
| 20. Latitude and longitude of the contacts.srt | 4.2 KB | ||
| 21. Contact map using Basemap.mp4 | 66.2 MB | ||
| 21. Contact map using Basemap.srt | 9.3 KB | ||
| 22. Getting countries and cities.mp4 | 52.7 MB | ||
| 22. Getting countries and cities.srt | 6.5 KB | ||
| 23. Graph of users by countries and cities.mp4 | 71.7 MB | ||
| 23. Graph of users by countries and cities.srt | 8.5 KB | ||
| 24. Introduction to clustering.mp4 | 14 MB | ||
| 24. Introduction to clustering.srt | 2.8 KB | ||
| 25. Introduction to k-mean algorithm.mp4 | 12.5 MB | ||
| 25. Introduction to k-mean algorithm.srt | 4.6 KB | ||
| 26. Clustering users by location with k-means.mp4 | 62.3 MB | ||
| 26. Clustering users by location with k-means.srt | 7.6 KB | ||
| 27. Visualizing the clusters using Google Earth.mp4 | 95.2 MB | ||
| 27. Visualizing the clusters using Google Earth.srt | 7.5 KB | ||
| 28. Invitations dataset.mp4 | 16.3 MB | ||
| 28. Invitations dataset.srt | 3.1 KB | ||
| 29. HOMEWORK.html | 307.2 B | ||
| 3. Bonus course - valid until January 31st.html | 307.2 B | ||
| 3. HOMEWORK.html | 307.2 B | ||
| 3. LinkedIn API 1.mp4 | 73.8 MB | ||
| 3. LinkedIn API 1.srt | 13.7 KB | ||
| 3. Preprocessing the texts.mp4 | 84.9 MB | ||
| 3. Preprocessing the texts.srt | 14 KB | ||
| 30. Homework solution.mp4 | 20.8 MB | ||
| 30. Homework solution.srt | 2.7 KB | ||
| 31. Analysis of the invitations dataset.mp4 | 41.5 MB | ||
| 31. Analysis of the invitations dataset.srt | 5.2 KB | ||
| 4. Homework solution.mp4 | 27 MB | ||
| 4. Homework solution.srt | 3.9 KB | ||
| 4. LinkedIn API 2.mp4 | 104 MB | ||
| 4. LinkedIn API 2.srt | 11.8 KB | ||
| 4. Preprocessing the dataset.mp4 | 42.7 MB | ||
| 4. Preprocessing the dataset.srt | 7.4 KB | ||
| 5. Companies data.mp4 | 74.6 MB | ||
| 5. Companies data.srt | 10 KB | ||
| 5. Detecting languages.mp4 | 52.7 MB | ||
| 5. Detecting languages.srt | 10.5 KB | ||
| 5. Getting data from LinkedIn.mp4 | 14.7 MB | ||
| 5. Getting data from LinkedIn.srt | 2.7 KB | ||
| 6. Connections dataset.mp4 | 59.6 MB | ||
| 6. Connections dataset.srt | 11 KB | ||
| 6. Positions data.mp4 | 85.4 MB | ||
| 6. Positions data.srt | 8.4 KB | ||
| 6. Word cloud.mp4 | 33.3 MB | ||
| 6. Word cloud.srt | 3.6 KB | ||
| 7. Invitations dataset 1.mp4 | 28.4 MB | ||
| 7. Invitations dataset 1.srt | 5 KB | ||
| 7. Levenshtein distance.mp4 | 47.7 MB | ||
| 7. Levenshtein distance.srt | 6.5 KB | ||
| 7. Named entity recognition.mp4 | 38.5 MB | ||
| 7. Named entity recognition.srt | 5 KB | ||
| 8. Invitations dataset 2.mp4 | 64.1 MB | ||
| 8. Invitations dataset 2.srt | 10.6 KB | ||
| 8. N-gram similarity.mp4 | 80.5 MB | ||
| 8. N-gram similarity.srt | 10.8 KB | ||
| 8. Sentiment analysis.mp4 | 55.5 MB | ||
| 8. Sentiment analysis.srt | 9 KB | ||
| 9. Generating fake data.mp4 | 33.4 MB | ||
| 9. Generating fake data.srt | 5 KB | ||
| 9. Jaccard distance.mp4 | 74.5 MB | ||
| 9. Jaccard distance.srt | 8.4 KB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| Mining and Analyzing LinkedIn Data.pdf | 3.2 MB | ||
| _Course materials | 204.8 B | ||
| _Mining and Analyzing LinkedIn Data.pdf | 614.4 B | ||
| _clusters.kml | 614.4 B | ||
| _connections.csv | 307.2 B | ||
| _connections.kml | 614.4 B | ||
| _connections_location.csv | 307.2 B | ||
| _connections_locations_full.csv | 614.4 B | ||
| _data.json | 307.2 B | ||
| _invitations.csv | 614.4 B | ||
| _invitations_locations.csv | 307.2 B | ||
| _messages.csv | 307.2 B | ||
| clusters.kml | 3.4 KB | ||
| connections.csv | 94.9 KB | ||
| connections.kml | 270.7 KB | ||
| connections_location.csv | 124.9 KB | ||
| connections_locations_full.csv | 147.2 KB | ||
| data.json | 125.5 KB | ||
| invitations.csv | 5.5 KB | ||
| invitations_locations.csv | 15.1 KB | ||
| messages.csv | 184.3 KB | ||
| ▲ 128 total files | |||
Mining and Analyzing LinkedIn Data
https://DevCourseWeb.com
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.29 GB | Duration: 6h 18m
Apply Data Science and Artificial Intelligence techniques to extract and analyze your LinkedIn network
What you'll learn
Extract data from your LinkedIn profile using the LinkedIn API and .csv files
Extract and analyze the connections between users, invitations, and text messages
Generate fake usernames to mask real information
Explore and view data related to your contacts' companies and job titles
Use edit Levenshtein distance, n-gram similarity and Jaccard distance to measure similarity between strings
Cluster contacts based on similarity between positions, as well as generate HTML views to improve data presentation
Use location APIs to extract latitude and longitude of contacts, in order to capture the city and country of lives
View the location of contacts dynamically with Google Earth and the Basemap library
Cluster contacts using the k-means algorithm
Apply natural language processing techniques to analyze your LinkedIn text messages
Generate word cloud to view the most frequent terms
Extract name entities from text messages
Create a sentiment classifier to extract the polarity of the LinkedIn text messages
Requirements
Programming logic
Basic Python programming
No LinkedIn knowledge is necessary
Description
LinkedIn is a social network focused on professional experience in order to generate connections and relationships between professionals from different areas. Professionals can provide profissional skills and search for jobs by connecting with people around the world. For example, if you would like to work with Data Science you can connect with companies and people who work in this field, increasing your chances of getting a job. On the other hand, companies are able to search for candidates according to the curriculum and skills provided by users. In 2017, LinkedIn established itself as the largest business platform and an important strategic tool for both professionals and companies.
| torrent name | size | uploader | age | seed | leech |
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| 3.5 GB | freecoursewb | 3 months | 6 | 0 | |
| 370.8 MB | freecoursewb | 4 months | 0 | 0 | |
| 850.9 MB | freecoursewb | 2 years | 3 | 2 | |
| 3.2 GB | freecoursewb | 2 years | 0 | 0 | |
| 3.2 GB | freecoursewb | 4 years | 0 | 0 |
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