DesireCourse Net Udemy - Data Science Natural Language Processing NLP in Python

1. 1. Natural Language Processing - What is it used for/1. Introduction and Outline.mp42.51MB
2. 1. Natural Language Processing - What is it used for/2. NLP Applications.mp45.73MB
3. 1. Natural Language Processing - What is it used for/3. Why is NLP hard.mp47.14MB
4. 1. Natural Language Processing - What is it used for/4. The Central Message of this Course.mp43.14MB
5. 10. Machine Learning Basics Review/1. (Review) Machine Learning Section Introduction.mp444.86MB
6. 10. Machine Learning Basics Review/10. (Review) Machine Learning and Deep Learning Future Topics.mp440.87MB
7. 10. Machine Learning Basics Review/11. (Review) Section Summary.mp424.82MB
8. 10. Machine Learning Basics Review/2. (Review) What is Classification.mp470.66MB
9. 10. Machine Learning Basics Review/3. (Review) Classification in Code.mp4139.49MB
10. 10. Machine Learning Basics Review/4. (Review) What is Regression.mp449.56MB
11. 10. Machine Learning Basics Review/5. (Review) Regression in Code.mp469.36MB
12. 10. Machine Learning Basics Review/6. (Review) What is a Feature Vector.mp437.99MB
13. 10. Machine Learning Basics Review/7. (Review) Machine Learning is Nothing but Geometry.mp422.74MB
14. 10. Machine Learning Basics Review/8. (Review) All Data is the Same.mp424.81MB
15. 10. Machine Learning Basics Review/9. (Review) Comparing Different Machine Learning Models.mp453.56MB
16. 11. Appendix FAQ/1. What is the Appendix.mp45.45MB
17. 11. Appendix FAQ/10. What order should I take your courses in (part 1).mp429.32MB
18. 11. Appendix FAQ/11. What order should I take your courses in (part 2).mp437.63MB
19. 11. Appendix FAQ/12. BONUS Where to get discount coupons and FREE deep learning material.mp437.83MB
20. 11. Appendix FAQ/2. Windows-Focused Environment Setup 2018.mp4186.47MB
21. 11. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp443.92MB
22. 11. Appendix FAQ/4. How to Code by Yourself (part 1).mp424.54MB
23. 11. Appendix FAQ/5. How to Code by Yourself (part 2).mp414.8MB
24. 11. Appendix FAQ/6. How to Succeed in this Course (Long Version).mp412.99MB
25. 11. Appendix FAQ/7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp438.95MB
26. 11. Appendix FAQ/8. Proof that using Jupyter Notebook is the same as not using it.mp478.28MB
27. 11. Appendix FAQ/9. Python 2 vs Python 3.mp47.84MB
28. 2. Course Preparation/1. How to Succeed in this Course.mp43.31MB
29. 2. Course Preparation/2. Where to get the code and data.mp44.44MB
30. 2. Course Preparation/3. Do you need a review of machine learning.mp413.4MB
31. 3. Decrypting Ciphers/1. Section Introduction.mp426.32MB
32. 3. Decrypting Ciphers/10. Code pt 4.mp425.69MB
33. 3. Decrypting Ciphers/11. Code pt 5.mp440.93MB
34. 3. Decrypting Ciphers/12. Code pt 6.mp439.35MB
35. 3. Decrypting Ciphers/13. Section Conclusion.mp424.26MB
36. 3. Decrypting Ciphers/2. Ciphers.mp417.19MB
37. 3. Decrypting Ciphers/3. Language Models.mp465.65MB
38. 3. Decrypting Ciphers/4. Genetic Algorithms.mp4105.19MB
39. 3. Decrypting Ciphers/5. Code Preparation.mp420.65MB
40. 3. Decrypting Ciphers/7. Code pt 1.mp415.9MB
41. 3. Decrypting Ciphers/8. Code pt 2.mp439.15MB
42. 3. Decrypting Ciphers/9. Code pt 3.mp429.55MB
43. 4. Build your own spam detector/1. Build your own spam detector - description of data.mp41.91MB
44. 4. Build your own spam detector/10. SMS Spam in Code.mp413.92MB
45. 4. Build your own spam detector/2. Build your own spam detector using Naive Bayes and AdaBoost - the code.mp46.55MB
46. 4. Build your own spam detector/3. Key Takeaway from Spam Detection Exercise.mp430.58MB
47. 4. Build your own spam detector/4. Naive Bayes Concepts.mp439.65MB
48. 4. Build your own spam detector/5. AdaBoost Concepts.mp426.75MB
49. 4. Build your own spam detector/6. Other types of features.mp41.42MB
50. 4. Build your own spam detector/7. Spam Detection FAQ (Remedial #1).mp410.66MB
51. 4. Build your own spam detector/8. What is a Vector (Remedial #2).mp49.07MB
52. 4. Build your own spam detector/9. SMS Spam Example.mp45.71MB
53. 5. Build your own sentiment analyzer/1. Description of Sentiment Analyzer.mp45.07MB
54. 5. Build your own sentiment analyzer/2. Logistic Regression Review.mp412.2MB
55. 5. Build your own sentiment analyzer/3. Preprocessing Tokenization.mp47.74MB
56. 5. Build your own sentiment analyzer/4. Preprocessing Tokens to Vectors.mp410.56MB
57. 5. Build your own sentiment analyzer/5. Sentiment Analysis in Python using Logistic Regression.mp443.54MB
58. 5. Build your own sentiment analyzer/6. Sentiment Analysis Extension.mp45.16MB
59. 5. Build your own sentiment analyzer/7. How to Improve Sentiment Analysis & FAQ.mp477.73MB
60. 6. NLTK Exploration/1. NLTK Exploration POS Tagging.mp42.03MB
61. 6. NLTK Exploration/2. NLTK Exploration Stemming and Lemmatization.mp43.66MB
62. 6. NLTK Exploration/3. NLTK Exploration Named Entity Recognition.mp46.67MB
63. 6. NLTK Exploration/4. Want more NLTK.mp48.45MB
64. 7. Latent Semantic Analysis/1. Latent Semantic Analysis - What does it do.mp43.93MB
65. 7. Latent Semantic Analysis/2. SVD - The underlying math behind LSA.mp478.22MB
66. 7. Latent Semantic Analysis/3. Latent Semantic Analysis in Python.mp425.44MB
67. 7. Latent Semantic Analysis/4. What is Latent Semantic Analysis Used For.mp416.88MB
68. 7. Latent Semantic Analysis/5. Extending LSA.mp410.91MB
69. 8. Write your own article spinner/1. Article Spinning Introduction and Markov Models.mp44.66MB
70. 8. Write your own article spinner/2. Trigram Model.mp43.86MB
71. 8. Write your own article spinner/3. More about Language Models.mp436.63MB
72. 8. Write your own article spinner/4. Precode Exercises.mp419.78MB
73. 8. Write your own article spinner/5. Writing an article spinner in Python.mp425.95MB
74. 8. Write your own article spinner/6. Article Spinner Extension Exercises.mp423.67MB
75. 9. How to learn more about NLP/1. What we didn't talk about.mp44.32MB