| 0 | 274 KB | ||
| 1. Assignment operator in R.mp4 | 15.7 MB | ||
| 1. Assignment operator in R.srt | 1.3 KB | ||
| 1. Important Terms in Text Mining.mp4 | 34.9 MB | ||
| 1. Important Terms in Text Mining.srt | 2.8 KB | ||
| 1. Interface of R-studio.mp4 | 60.4 MB | ||
| 1. Interface of R-studio.srt | 11 KB | ||
| 1. Introduction.mp4 | 17.5 MB | ||
| 1. Introduction.srt | 1.2 KB | ||
| 1. Loops in R.mp4 | 26.8 MB | ||
| 1. Loops in R.srt | 2.3 KB | ||
| 1. Text Mining in R.mp4 | 83.8 MB | ||
| 1 | 480.5 KB | ||
| 1. Math in R.mp4 | 19.6 MB | ||
| 1. Math in R.srt | 5.6 KB | ||
| 1. Text Mining in R.srt | 7.4 KB | ||
| 1. Theory Introduction to the dplyr package.mp4 | 19.8 MB | ||
| 1. Theory Introduction to the dplyr package.srt | 1.7 KB | ||
| 1. Tools for webscraping in R.mp4 | 119.6 MB | ||
| 1. Tools for webscraping in R.srt | 11.6 KB | ||
| 2. Common Techniques.mp4 | 87.6 MB | ||
| 2. Installing rvest package in R.mp4 | 54.4 MB | ||
| 2. Installing rvest package in R.srt | 8.9 KB | ||
| 2 | 313.5 KB | ||
| 2. Common Techniques.srt | 6.7 KB | ||
| 2. Miscellaneous operators in R.mp4 | 46.7 MB | ||
| 2. Miscellaneous operators in R.srt | 8.5 KB | ||
| 2. No background required!!.mp4 | 9.3 MB | ||
| 2. No background required!!.srt | 819.2 B | ||
| 2. Select function in R.mp4 | 40.5 MB | ||
| 2. Select function in R.srt | 3.4 KB | ||
| 2. Theory Concepts of loops.mp4 | 33.6 MB | ||
| 2. Theory Concepts of loops.srt | 2.6 KB | ||
| 2. Theory Installing packages in R.mp4 | 81.5 MB | ||
| 2. Theory Installing packages in R.srt | 6.6 KB | ||
| 2. Theory Vectors in R.mp4 | 78.1 MB | ||
| 2. Theory Vectors in R.srt | 6.2 KB | ||
| 2. What is web scraping.mp4 | 91.2 MB | ||
| 2. What is web scraping.srt | 7.5 KB | ||
| 3. Access vector items.mp4 | 71.2 MB | ||
| 3 | 845.7 KB | ||
| 3. Access vector items.srt | 6 KB | ||
| 3. Installing packages in r.mp4 | 40.1 MB | ||
| 3. Installing packages in r.srt | 7.7 KB | ||
| 3. Read html contents.mp4 | 55.1 MB | ||
| 3. Read html contents.srt | 6.9 KB | ||
| 3. Select function in R.mp4 | 54 MB | ||
| 3. Select function in R.srt | 9.2 KB | ||
| 3. Tokenization in R.mp4 | 43.6 MB | ||
| 3. Tokenization in R.srt | 4.9 KB | ||
| 3. What will you learn.mp4 | 8.6 MB | ||
| 3. What will you learn.srt | 614.4 B | ||
| 3. table function in R.mp4 | 16.9 MB | ||
| 3. table function in R.srt | 3.5 KB | ||
| 3. while loop in R.mp4 | 12.7 MB | ||
| 3. while loop in R.srt | 3.8 KB | ||
| 4. Filter function in R.mp4 | 51.6 MB | ||
| 4. Filter function in R.srt | 4.2 KB | ||
| 4. Stemming in R.mp4 | 44.7 MB | ||
| 4. Stemming in R.srt | 6.2 KB | ||
| 4 | 366.6 KB | ||
| 4. Data types in R.mp4 | 48.8 MB | ||
| 4. Data types in R.srt | 6.2 KB | ||
| 4. Generating sequenced vector.mp4 | 31.6 MB | ||
| 4. Generating sequenced vector.srt | 2.8 KB | ||
| 4. Use locator to get html nodes.mp4 | 74 MB | ||
| 4. Use locator to get html nodes.srt | 8.8 KB | ||
| 4. What is R.mp4 | 46.3 MB | ||
| 4. What is R.srt | 3.6 KB | ||
| 4. for loop in R.mp4 | 32.5 MB | ||
| 4. for loop in R.srt | 7.2 KB | ||
| 5. Assignment operator in R.mp4 | 17.6 MB | ||
| 5. Assignment operator in R.srt | 1.4 KB | ||
| 5. Natural Language Processing.mp4 | 62.7 MB | ||
| 5. Natural Language Processing.srt | 5 KB | ||
| 5. The apply function in R.mp4 | 26.1 MB | ||
| 5. The apply function in R.srt | 2.3 KB | ||
| 5. Using dplyr.mp4 | 38 MB | ||
| 5. Vectors in R.mp4 | 76.4 MB | ||
| 5. Vectors in R.srt | 14.7 KB | ||
| 5 | 479.9 KB | ||
| 6 | 279.9 KB | ||
| 7 | 836.9 KB | ||
| 8 | 311.4 KB | ||
| 9 | 419.4 KB | ||
| 10. Clean data continued...mp4 | 124.2 MB | ||
| 10. Clean data continued...srt | 13.3 KB | ||
| 10. Data Types and Type-casting.mp4 | 23.3 MB | ||
| 10. Data Types and Type-casting.srt | 6 KB | ||
| 10. Matrices in R.mp4 | 46.9 MB | ||
| 10. Matrices in R.srt | 8.3 KB | ||
| 10. Pipe operator in R (Do not miss this video).mp4 | 50.9 MB | ||
| 10. Pipe operator in R (Do not miss this video).srt | 10.3 KB | ||
| 11. Filter the data.mp4 | 30.2 MB | ||
| 11. Filter the data.srt | 4.5 KB | ||
| 11. Relational data.mp4 | 57.9 MB | ||
| 11. Relational data.srt | 4.2 KB | ||
| 12. Data Frames in R.mp4 | 52.2 MB | ||
| 12. Data Frames in R.srt | 4.1 KB | ||
| 12. Get content using scraping.mp4 | 114.5 MB | ||
| 12. Get content using scraping.srt | 12 KB | ||
| 13. Split the data.mp4 | 55.8 MB | ||
| 13. Split the data.srt | 7.5 KB | ||
| 13. Theory Access items from data frame.mp4 | 32.3 MB | ||
| 13. Theory Access items from data frame.srt | 2.5 KB | ||
| 14. Add rows to the data frame.mp4 | 29.8 MB | ||
| 14. Add rows to the data frame.srt | 2.3 KB | ||
| 14. Use loops for repeated tasks.mp4 | 51.8 MB | ||
| 14. Use loops for repeated tasks.srt | 8.6 KB | ||
| 15. Add columns to the data frame.mp4 | 26.8 MB | ||
| 15. Add columns to the data frame.srt | 2.1 KB | ||
| 15. Creating data frame.mp4 | 45.7 MB | ||
| 15. Creating data frame.srt | 6.8 KB | ||
| 16. Data Frame in R.mp4 | 56.8 MB | ||
| 16. Data Frame in R.srt | 9.8 KB | ||
| 16. Refine the data from data frame.mp4 | 84.8 MB | ||
| 16. Refine the data from data frame.srt | 12.9 KB | ||
| 17. Count rows and columns.mp4 | 14 MB | ||
| 17. Count rows and columns.srt | 2.9 KB | ||
| 17. Use data frame.mp4 | 67.7 MB | ||
| 17. Use data frame.srt | 11.2 KB | ||
| 18. Factor in R.mp4 | 33 MB | ||
| 18. Factor in R.srt | 5.6 KB | ||
| 18. Theory What is corpus.mp4 | 65.4 MB | ||
| 18. Theory What is corpus.srt | 5.4 KB | ||
| 19. Theory Term Document Matrix.mp4 | 16.6 MB | ||
| 19. Theory Term Document Matrix.srt | 1.2 KB | ||
| 20. Theory Bag of Word Models.mp4 | 34.8 MB | ||
| 20. Theory Bag of Word Models.srt | 3.1 KB | ||
| 21. Theory Vector Space Model.mp4 | 79.3 MB | ||
| 21. Theory Vector Space Model.srt | 7.5 KB | ||
| 22. Term Frequency -- IMPORTANT.mp4 | 55.1 MB | ||
| 22. Term Frequency -- IMPORTANT.srt | 5.4 KB | ||
| 23. Inverse Document Frequency model.mp4 | 57.8 MB | ||
| 23. Inverse Document Frequency model.srt | 5.9 KB | ||
| 24. Corpus and Term Document Matrix.mp4 | 73.5 MB | ||
| 24. Corpus and Term Document Matrix.srt | 12.4 KB | ||
| 25. Remove Sparse terms.mp4 | 102.2 MB | ||
| 25. Remove Sparse terms.srt | 11.6 KB | ||
| 26. Frequency distributions.mp4 | 77.9 MB | ||
| 26. Frequency distributions.srt | 11.9 KB | ||
| 27. Theory Wordclouds in R.mp4 | 49.1 MB | ||
| 27. Theory Wordclouds in R.srt | 4 KB | ||
| 28. Wordcloud.mp4 | 47.7 MB | ||
| 28. Wordcloud.srt | 5.7 KB | ||
| 29. Clean the corpus.mp4 | 48.2 MB | ||
| 29. Clean the corpus.srt | 8.3 KB | ||
| 30. Remove stop words.mp4 | 127.7 MB | ||
| 30. Remove stop words.srt | 17.2 KB | ||
| 31. Season 2 of Big Bang Theory.mp4 | 62.1 MB | ||
| 31. Season 2 of Big Bang Theory.srt | 4.8 KB | ||
| 32. Frequency distributions.mp4 | 77.6 MB | ||
| 32. Frequency distributions.srt | 11.9 KB | ||
| 33. Plot a bar graph.mp4 | 78.1 MB | ||
| 33. Plot a bar graph.srt | 11 KB | ||
| 34. Add theme to bar graph.mp4 | 65.6 MB | ||
| 34. Add theme to bar graph.srt | 8.8 KB | ||
| 35. What is sentimental analysis.mp4 | 52 MB | ||
| 35. What is sentimental analysis.srt | 3.6 KB | ||
| 36. How sentimental analysis work.mp4 | 65.5 MB | ||
| 36. How sentimental analysis work.srt | 4.6 KB | ||
| 37. Bing and NRC lexicon.mp4 | 45.8 MB | ||
| 37. Bing and NRC lexicon.srt | 4.3 KB | ||
| 38. How sentiments classification is done.mp4 | 31 MB | ||
| 38. How sentiments classification is done.srt | 3.9 KB | ||
| 39. Tokenization.mp4 | 52.1 MB | ||
| 39. Tokenization.srt | 8 KB | ||
| 40. Theory Reshape in R.mp4 | 86.1 MB | ||
| 40. Theory Reshape in R.srt | 6.4 KB | ||
| 41. Melting in R.mp4 | 87.4 MB | ||
| 41. Melting in R.srt | 13.1 KB | ||
| 42. Casting in R.mp4 | 59.7 MB | ||
| 42. Casting in R.srt | 9.2 KB | ||
| 43. Using bing lexicon.mp4 | 96.6 MB | ||
| 43. Using bing lexicon.srt | 12 KB | ||
| 44. Using NRC lexicon.mp4 | 144.5 MB | ||
| 44. Using NRC lexicon.srt | 16.5 KB | ||
| 45. Plot ribbon plots.mp4 | 73.2 MB | ||
| 45. Plot ribbon plots.srt | 10.3 KB | ||
| 5. Filter function in R.mp4 | 31.7 MB | ||
| 5. Filter function in R.srt | 4.5 KB | ||
| 5. Using dplyr.srt | 5.4 KB | ||
| 6. Create multiple variables in R.mp4 | 12.2 MB | ||
| 6. Create multiple variables in R.srt | 921.6 B | ||
| 6. Data Manipulation.mp4 | 52.3 MB | ||
| 6. Data Manipulation.srt | 6.5 KB | ||
| 6. Text Mining Applications.mp4 | 55.6 MB | ||
| 6. Text Mining Applications.srt | 4.7 KB | ||
| 6. Theory Function in R.mp4 | 103.7 MB | ||
| 6. Theory Function in R.srt | 9.3 KB | ||
| 6. Theory List in R.mp4 | 62.7 MB | ||
| 6. Theory List in R.srt | 4.8 KB | ||
| 6. Theory Mutate and Transmute function in R.mp4 | 75.4 MB | ||
| 6. Theory Mutate and Transmute function in R.srt | 6.4 KB | ||
| 7. Change column name.mp4 | 20.2 MB | ||
| 7. Change column name.srt | 2.8 KB | ||
| 7. Check if item exists in list.mp4 | 21.2 MB | ||
| 7. Check if item exists in list.srt | 1.7 KB | ||
| 7. Concatenate variables in R.mp4 | 18.7 MB | ||
| 7. Concatenate variables in R.srt | 1.4 KB | ||
| 7. Functions in R.mp4 | 54.2 MB | ||
| 7. Functions in R.srt | 10.9 KB | ||
| 7. Mutate and Transmute function in R.mp4 | 30.9 MB | ||
| 7. Mutate and Transmute function in R.srt | 5.6 KB | ||
| 8. Add item to the list.mp4 | 43.2 MB | ||
| 8. Add item to the list.srt | 3.7 KB | ||
| 8. Default argument in R.mp4 | 40.2 MB | ||
| 8. Default argument in R.srt | 3.2 KB | ||
| 8. Get all links.mp4 | 65.1 MB | ||
| 8. Get all links.srt | 7.5 KB | ||
| 8. The diff() function in R.mp4 | 33.4 MB | ||
| 8. The diff() function in R.srt | 3.2 KB | ||
| 8. Variables in R.mp4 | 41.2 MB | ||
| 8. Variables in R.srt | 9 KB | ||
| 9. Cleaning the data.mp4 | 100.7 MB | ||
| 9. Cleaning the data.srt | 11.9 KB | ||
| 9. List in R.mp4 | 58.1 MB | ||
| 9. List in R.srt | 10.6 KB | ||
| 9. Rule for naming a variable.mp4 | 61.1 MB | ||
| 9. Rule for naming a variable.srt | 6 KB | ||
| 9. Theory Pipe operator in R.mp4 | 154 MB | ||
| 9. Theory Pipe operator in R.srt | 14.9 KB | ||
| TutsNode.com.txt | 102.4 B | ||
| [TGx]Downloaded from torrentgalaxy.to .txt | 614.4 B | ||
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| ▲ 316 total files | |||
Description
Caution before taking this course:
This course does not make you expert in R programming rather it will teach you concepts which will be more than enough to be used in machine learning and natural language processing models.
About the course:
In this practical, hands-on course you’ll learn how to program in R and how to use R for effective data analysis, visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.
Our main objective is to give you the education not just to understand the ins and outs of the R programming language, but also to learn exactly how to become a professional Data Scientist with R and land your first job.
This course covers following topics:
1. R programming concepts: variables, data structures: vector, matrix, list, data frames/ loops/ functions/ dplyr package/ apply() functions
2. Web scraping: How to scrape titles, link and store to the data structures
3. NLP technologies: Bag of Word model, Term Frequency model, Inverse Document Frequency model
4. Sentimental Analysis: Bing and NRC lexicon
5. Text mining
By the end of the course you’ll be in a journey to become Data Scientist with R and confidently apply for jobs and feel good knowing that you have the skills and knowledge to back it up.
Who this course is for:
You should take this course if you want to become a Data Scientist or if you want to learn about the field
You should take this course if you want to learn text mining and text analysis doing fun projects
You should take this course if you want to learn web scraping
Requirements
No programming experiences required
No R programming experience required
Machine with any OS (Linux, MacOSX, Windows) and proper internet connection required
Last Updated 10/2021
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