LinkedIn - Data Science Foundations: Data Mining in Python

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size638.5 MB
  • Uploaded Bytutsnode
  • Downloads236
  • Last checkedApr. 19th '21
  • Date uploadedApr. 16th '21
  • Seeders 27
  • Leechers3

Infohash : E1C971358E6653D5D511F75D3BE100913E97772F


Description

Data mining is the area of data science that focuses on finding actionable patterns in large and diverse datasets: clusters of similar customers, trends over time that can only be spotted after disentangling seasonal and random effects, and new methods for predicting important outcomes. In this course, instructor Barton Poulson introduces you to data mining that uses the programming language Python. Barton goes over some preliminaries, such as the tools you may use for data mining. He discusses aspects of dimensionality reduction, then explains clustering, including hierarchical clustering, k-Means, DBSCAN, and more. Barton covers classification, including kNN and decision trees. He goes into association analysis and introduces you to Apriori, Eclat, and FP-Growth. Barton steps you through a time-series decomposition, then concludes with sentiment scoring and other text mining tools.

Files:

Data Science Foundations Data Mining in Python [TutsNode.com] - Data Science Foundations Data Mining in Python
  • 7. Validating results.mp4 (28.3 MB)
  • 15. Clustering overview.mp4 (28.0 MB)
  • 22. Classification overview.mp4 (25.9 MB)
  • 39. ARIMA.mp4 (25.8 MB)
  • 4. Tools for data mining.mp4 (25.1 MB)
  • 8. Dimensionality reduction overview.mp4 (24.0 MB)
  • 29. Association analysis overview.mp4 (18.5 MB)
  • 40. MLP.mp4 (17.8 MB)
  • 5. The CRISP-DM data mining model.mp4 (17.7 MB)
  • 18. K-means.mp4 (17.5 MB)
  • 43. Text mining overview.mp4 (17.3 MB)
  • 6. Privacy, copyright, and bias.mp4 (17.2 MB)
  • 36. Time-series mining.mp4 (16.8 MB)
  • 31. Apriori.mp4 (16.1 MB)
  • 9. Handwritten digits dataset.mp4 (15.3 MB)
  • 45. Sentiment analysis- Binary classification.mp4 (15.0 MB)
  • 32. Eclat.mp4 (14.7 MB)
  • 50. Next steps.mp4 (14.2 MB)
  • 35. Solution- Apriori.mp4 (14.1 MB)
  • 12. t-SNE.mp4 (13.5 MB)
  • 46. Sentiment analysis- Sentiment scoring.mp4 (13.4 MB)
  • 24. KNN.mp4 (13.2 MB)
  • 33. FP-Growth.mp4 (13.2 MB)
  • 47. Word pairs.mp4 (13.1 MB)
  • 26. Decision trees.mp4 (12.4 MB)
  • 19. DBSCAN.mp4 (12.3 MB)
  • 10. PCA.mp4 (12.3 MB)
  • 28. Solution- KNN.mp4 (12.0 MB)
  • 23. Spambase dataset.mp4 (11.5 MB)
  • 11. LDA.mp4 (10.8 MB)
  • 38. Time-Series decomposition.mp4 (10.8 MB)
  • 49. Solution- Sentiment scoring.mp4 (10.5 MB)
  • 16. Penguin dataset.mp4 (9.2 MB)
  • 17. Hierarchical clustering.mp4 (8.6 MB)
  • 21. Solution- K-means.mp4 (8.6 MB)
  • 25. Naive Bayes.mp4 (8.5 MB)
  • 42. Solution- Decomposition.mp4 (8.4 MB)
  • 41. Challenge- Decomposition.mp4 (6.9 MB)
  • 27. Challenge- KNN.mp4 (6.8 MB)
  • 37. Air Passengers dataset.mp4 (6.6 MB)
  • 14. Solution- PCA.mp4 (5.8 MB)
  • 30. Groceries dataset.mp4 (5.5 MB)
  • 13. Challenge- PCA.mp4 (5.2 MB)
  • 34. Challenge- Apriori.mp4 (4.9 MB)
  • 1. Python for data mining.mp4 (4.2 MB)
  • 20. Challenge- K-means.mp4 (3.4 MB)
  • 44. Iliad dataset.mp4 (2.9 MB)
  • 48. Challenge- Sentiment scoring.mp4 (2.7 MB)
  • 3. Exercise files.mp4 (1.8 MB)
  • 2. What you should know.mp4 (1.8 MB)
  • Ex_Files_Data_Mining_Python_R.zip (1.5 MB)
  • TutsNode.com.txt (0.1 KB)
  • [TGx]Downloaded from torrentgalaxy.to .txt (0.6 KB)
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