CourseClub Me O'REILLY - Python for Data Science Complete Video Course Video Training

1. 01 - Python for Data Science Complete Video Course Video Training - Introduction.mp476.64MB
2. 02 - Learning objectives.mp411.21MB
3. 03 - 1.1 History of Python in data science.mp478.08MB
4. 04 - 1.2 Overview of Python data science libraries.mp444.37MB
5. 05 - 1.3 Future trends of Python in AI, ML, and data science.mp477.54MB
6. 06 - Learning objectives.mp425MB
7. 07 - 2.1 Create your first Colab document.mp4328.82MB
8. 08 - 2.2 Manage Colab documents.mp4451.8MB
9. 09 - 2.3 Use magic functions.mp4156.26MB
10. 10 - 2.4 Understand compatibility with Jupyter.mp4258.05MB
11. 11 - Learning objectives.mp428.81MB
12. 12 - 3.1 Write procedural code.mp4112.86MB
13. 13 - 3.2 Use simple expressions and variables.mp4173.93MB
14. 14 - 3.3 Work with the built-in types.mp466.6MB
15. 15 - 3.4 Learn to Print.mp470.6MB
16. 16 - 3.5 Perform basic math operations.mp4167.11MB
17. 17 - 3.6 Use classes and objects with dot notation.mp4194.46MB
18. 18 - Learning objectives.mp417MB
19. 19 - 4.1 Use string methods.mp4131.93MB
20. 20 - 4.2 Format strings.mp498.69MB
21. 21 - 4.3 Manipulate strings - membership, slicing, and concatenation.mp4136.75MB
22. 22 - 4.4 Learn to use unicode.mp474.37MB
23. 23 - Learning objectives.mp422.45MB
24. 24 - 5.1 Use lists and tuples.mp4369.96MB
25. 25 - 5.2 Explore dictionaries.mp4213.33MB
26. 26 - 5.3 Dive into sets.mp483.03MB
27. 27 - 5.4 Work with the numpy array.mp4234.44MB
28. 28 - 5.5 Use the Pandas DataFrame.mp4116.78MB
29. 29 - 5.6 Use the Pandas Series.mp471.62MB
30. 30 - Learning objectives.mp424MB
31. 31 - 6.1 Convert lists to dicts and back.mp474.45MB
32. 32 - 6.2 Convert dicts to Pandas Dataframe.mp4104.57MB
33. 33 - 6.3 Convert characters to integers and back.mp435.73MB
34. 34 - 6.4 Convert between hexadecimal, binary, and floats.mp4101.36MB
35. 35 - Learning objectives.mp424.93MB
36. 36 - 7.1 Learn to loop with for loops.mp444.92MB
37. 37 - 7.2 Repeat with while loops.mp450.23MB
38. 38 - 7.3 Learn to handle exceptions.mp4111.94MB
39. 39 - 7.4 Use conditionals.mp4168.25MB
40. 40 - Learning objectives.mp422.46MB
41. 41 - 8.1 Write and use functions.mp4206.47MB
42. 42 - 8.2 Learn to use decorators.mp4210.94MB
43. 43 - 8.3 Compose closure functions.mp4132.86MB
44. 44 - 8.4 Use lambdas.mp4106.23MB
45. 45 - 8.5 Advanced Use of Functions.mp4319.02MB
46. 46 - Learning objectives.mp433.79MB
47. 47 - 9.1 Learn NumPy.mp4287.95MB
48. 48 - 9.2 Learn SciPy.mp4664.99MB
49. 49 - 9.3 Learn Pandas.mp4335.61MB
50. 50 - 9.4 Learn TensorFlow.mp4341.9MB
51. 51 - 9.5 Use Seaborn for 2D plots.mp4261.65MB
52. 52 - 9.6 Use Plotly for interactive plots.mp4262.06MB
53. 53 - 9.7 Specialized Visualization Libraries.mp4241.69MB
54. 54 - 9.8 Learn Natural Language Processing Libraries.mp4124.95MB
55. 55 - Learning objectives.mp427.7MB
56. 56 - 10.1 Understand functional programming.mp4151.13MB
57. 57 - 10.2 Apply functions to data science workflows.mp447.12MB
58. 58 - 10.3 Use map_reduce_filter.mp495.23MB
59. 59 - 10.4 Use list comprehensions.mp498.27MB
60. 60 - 10.5 Use dictionary comprehensions.mp415.45MB
61. 61 - Learning objectives.mp417.83MB
62. 62 - 11.1 Use generators.mp469.4MB
63. 63 - 11.2 Design generator pipelines.mp4141.25MB
64. 64 - 11.3 Implement lazy evaluation functions.mp459.14MB
65. 65 - Learning objectives.mp420.97MB
66. 66 - 12.1 Perform simple pattern matching.mp497.05MB
67. 67 - 12.2 Use regular expressions.mp4284.59MB
68. 68 - 12.3 Learn text processing techniques - Beautiful Soup.mp487.6MB
69. 69 - Learning objectives.mp418.2MB
70. 70 - 13.1 Sort in Python.mp4186.66MB
71. 71 - 13.2 Create custom sorting functions.mp4229.33MB
72. 72 - 13.3 Sort in Pandas.mp4301.95MB
73. 73 - Learning objectives.mp422.1MB
74. 74 - 14.1 Read and write files - file, pickle, CSV, JSON.mp4214.71MB
75. 75 - 14.2 Read and write with Pandas - CSV, JSON.mp4336.5MB
76. 76 - 14.3 Read and write using web resources (requests, boto, github).mp4110.86MB
77. 77 - 14.4 Use function-based concurrency.mp4608.14MB
78. 78 - Learning objectives.mp420.91MB
79. 79 - 15.1 Share with Github.mp4358.09MB
80. 80 - 15.2 Create Kaggle Kernels.mp4207.48MB
81. 81 - 15.3 Collaborate with Colab.mp4125.18MB
82. 82 - 15.4 Post public graphs with Plotly.mp4103.5MB
83. 83 - Learning Objectives.mp428.71MB
84. 84 - 16.1 PyTest.mp4372.92MB
85. 85 - 16.2 Visual Studio Code.mp4364.64MB
86. 86 - 16.3 Vim.mp4136.81MB
87. 87 - 16.4 Ludwig (Open Source AutoML).mp4146.48MB
88. 88 - 16.5 Sklearn Algorithm Cheatsheet.mp4104.05MB
89. 89 - 16.6 Recommendations.mp447.75MB