Total Training - Machine Learning - Python Programming From Beginner to Intermediate

1. 01 - Introduction.mp457.46MB
2. 02 - Coding is Like Cooking.mp4127.09MB
3. 03 - Anaconda & Pip.mp437.62MB
4. 04 - Variables are Like Containers.mp460.22MB
5. 05 - A List is a List.mp445.98MB
6. 06 - Fun with Lists!.mp428.89MB
7. 07 - Dictionaries & If-Else.mp439.09MB
8. 08 - Don't Jump through Hoops - Use Loops.mp419.92MB
9. 09 - Doing Stuff with Loops.mp435.66MB
10. 10 - Everything in Life is a List - Strings as Lists.mp422.68MB
11. 11 - Modules are Cool for Code-Reuse.mp410.2MB
12. 12 - Our First Serious Program - Downloading a Webpage.mp463.74MB
13. 13 - A Few Details - Conditionals.mp442.42MB
14. 14 - A Few Details - Exception Handling in Python.mp443.09MB
15. 15 - A File is Like a Barrel.mp467.59MB
16. 16 - Auto-Generating Spreadsheets with Python.mp446.8MB
17. 17 - Auto-Generating Spreadsheets - Download & Unzip.mp472.2MB
18. 18 - Auto-Generating Spreadsheets - Parcing CSV Files.mp459.03MB
19. 19 - Auto-Generating Spreadsheets with XLSXwriter.mp415.36MB
20. 20 - Functions are Like Food Processors.mp451.69MB
21. 21 - Argument Passing in Functions.mp476.37MB
22. 22 - Writing Your First Function.mp444.01MB
23. 23 - Recursion.mp485.55MB
24. 24 - Recursion in Action.mp421.56MB
25. 25 - How Would You Implement a Bank ATM.mp494.49MB
26. 26 - Things You Can Do with Databases - I.mp494.4MB
27. 27 - Things You Can Do with Databases - II.mp441.07MB
28. 28 - Interfacing with Databases from Python.mp442.86MB
29. 29 - SQLite Works Right out of the Box.mp427.21MB
30. 30 - Manually Downloading the ZIP Files Required.mp411.73MB
31. 31 - Build a Database of Stock Movements - I.mp4101.34MB
32. 32 - Build a Database of Stock Movements - II.mp467.18MB
33. 33 - Build a Database of Stock Movements - III.mp462.9MB
34. 34 - Objects are Like Puppies!.mp446.14MB
35. 35 - A Class is a Type of Variable.mp493.65MB
36. 36 - An Interface Drives Behavior.mp469.64MB
37. 37 - Natural Language Processing with NLTK.mp433.56MB
38. 38 - Natural Language Processing with NLTK - See It in Action.mp451.91MB
39. 39 - Web Scraping with BeautifulSoup.mp467.16MB
40. 40 - A Serious NLP Application - Text Auto-Summarization Using Python.mp459.01MB
41. 41 - Auto-Summarize News Articles - I.mp456.02MB
42. 42 - Auto-Summarize News Articles - II.mp453.58MB
43. 43 - Auto-Summarize News Articles - III.mp4126.52MB
44. 44 - Machine Learning - Jump on the Bandwagon.mp477.52MB
45. 45 - Plunging In - Machine Learning Approaches to Spam Detection.mp475.07MB
46. 46 - Spam Detection with Machine Learning - Continued.mp489.96MB
47. 47 - News Article Classification Using K-Nearest Neighbors.mp498.25MB
48. 48 - News Article Classification Using Naive Bayes.mp494.75MB
49. 49 - Code Along - Scraping News Websites.mp454.28MB
50. 50 - Code Along - Feature Extraction from News Articles.mp458.45MB
51. 51 - Code Along - Classification with K-Nearest Neighbors.mp414.83MB
52. 52 - Code Along - Classification with Naïve Bayes.mp429.25MB
53. 53 - Document Distance Using TF-IDF.mp451.79MB
54. 54 - News Article Clustering with K-Means & TF-IDF.mp467.03MB
55. 55 - Code Along - Clustering with K-Means.mp437.75MB