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种子名称： [CourseClub.Me] O'REILLY - Python for Data Science Complete Video Course Video Training
文件类型： 视频
文件数目： 89个文件
文件大小： 13.13 GB
收录时间： 2020-2-9 16:20
已经下载： 3次
资源热度： 228
最近下载： 2024-6-2 08:45

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[CourseClub.Me] O'REILLY - Python for Data Science Complete Video Course Video Training.torrent

01 - Python for Data Science Complete Video Course Video Training - Introduction.mp476.64MB

02 - Learning objectives.mp411.21MB

03 - 1.1 History of Python in data science.mp478.08MB

04 - 1.2 Overview of Python data science libraries.mp444.37MB

05 - 1.3 Future trends of Python in AI, ML, and data science.mp477.54MB

06 - Learning objectives.mp425MB

07 - 2.1 Create your first Colab document.mp4328.82MB

08 - 2.2 Manage Colab documents.mp4451.8MB

09 - 2.3 Use magic functions.mp4156.26MB

10 - 2.4 Understand compatibility with Jupyter.mp4258.05MB

11 - Learning objectives.mp428.81MB

12 - 3.1 Write procedural code.mp4112.86MB

13 - 3.2 Use simple expressions and variables.mp4173.93MB

14 - 3.3 Work with the built-in types.mp466.6MB

15 - 3.4 Learn to Print.mp470.6MB

16 - 3.5 Perform basic math operations.mp4167.11MB

17 - 3.6 Use classes and objects with dot notation.mp4194.46MB

18 - Learning objectives.mp417MB

19 - 4.1 Use string methods.mp4131.93MB

20 - 4.2 Format strings.mp498.69MB

21 - 4.3 Manipulate strings - membership, slicing, and concatenation.mp4136.75MB

22 - 4.4 Learn to use unicode.mp474.37MB

23 - Learning objectives.mp422.45MB

24 - 5.1 Use lists and tuples.mp4369.96MB

25 - 5.2 Explore dictionaries.mp4213.33MB

26 - 5.3 Dive into sets.mp483.03MB

27 - 5.4 Work with the numpy array.mp4234.44MB

28 - 5.5 Use the Pandas DataFrame.mp4116.78MB

29 - 5.6 Use the Pandas Series.mp471.62MB

30 - Learning objectives.mp424MB

31 - 6.1 Convert lists to dicts and back.mp474.45MB

32 - 6.2 Convert dicts to Pandas Dataframe.mp4104.57MB

33 - 6.3 Convert characters to integers and back.mp435.73MB

34 - 6.4 Convert between hexadecimal, binary, and floats.mp4101.36MB

35 - Learning objectives.mp424.93MB

36 - 7.1 Learn to loop with for loops.mp444.92MB

37 - 7.2 Repeat with while loops.mp450.23MB

38 - 7.3 Learn to handle exceptions.mp4111.94MB

39 - 7.4 Use conditionals.mp4168.25MB

40 - Learning objectives.mp422.46MB

41 - 8.1 Write and use functions.mp4206.47MB

42 - 8.2 Learn to use decorators.mp4210.94MB

43 - 8.3 Compose closure functions.mp4132.86MB

44 - 8.4 Use lambdas.mp4106.23MB

45 - 8.5 Advanced Use of Functions.mp4319.02MB

46 - Learning objectives.mp433.79MB

47 - 9.1 Learn NumPy.mp4287.95MB

48 - 9.2 Learn SciPy.mp4664.99MB

49 - 9.3 Learn Pandas.mp4335.61MB

50 - 9.4 Learn TensorFlow.mp4341.9MB

51 - 9.5 Use Seaborn for 2D plots.mp4261.65MB

52 - 9.6 Use Plotly for interactive plots.mp4262.06MB

53 - 9.7 Specialized Visualization Libraries.mp4241.69MB

54 - 9.8 Learn Natural Language Processing Libraries.mp4124.95MB

55 - Learning objectives.mp427.7MB

56 - 10.1 Understand functional programming.mp4151.13MB

57 - 10.2 Apply functions to data science workflows.mp447.12MB

58 - 10.3 Use map_reduce_filter.mp495.23MB

59 - 10.4 Use list comprehensions.mp498.27MB

60 - 10.5 Use dictionary comprehensions.mp415.45MB

61 - Learning objectives.mp417.83MB

62 - 11.1 Use generators.mp469.4MB

63 - 11.2 Design generator pipelines.mp4141.25MB

64 - 11.3 Implement lazy evaluation functions.mp459.14MB

65 - Learning objectives.mp420.97MB

66 - 12.1 Perform simple pattern matching.mp497.05MB

67 - 12.2 Use regular expressions.mp4284.59MB

68 - 12.3 Learn text processing techniques - Beautiful Soup.mp487.6MB

69 - Learning objectives.mp418.2MB

70 - 13.1 Sort in Python.mp4186.66MB

71 - 13.2 Create custom sorting functions.mp4229.33MB

72 - 13.3 Sort in Pandas.mp4301.95MB

73 - Learning objectives.mp422.1MB

74 - 14.1 Read and write files - file, pickle, CSV, JSON.mp4214.71MB

75 - 14.2 Read and write with Pandas - CSV, JSON.mp4336.5MB

76 - 14.3 Read and write using web resources (requests, boto, github).mp4110.86MB

77 - 14.4 Use function-based concurrency.mp4608.14MB

78 - Learning objectives.mp420.91MB

79 - 15.1 Share with Github.mp4358.09MB

80 - 15.2 Create Kaggle Kernels.mp4207.48MB

81 - 15.3 Collaborate with Colab.mp4125.18MB

82 - 15.4 Post public graphs with Plotly.mp4103.5MB

83 - Learning Objectives.mp428.71MB

84 - 16.1 PyTest.mp4372.92MB

85 - 16.2 Visual Studio Code.mp4364.64MB

86 - 16.3 Vim.mp4136.81MB

87 - 16.4 Ludwig (Open Source AutoML).mp4146.48MB

88 - 16.5 Sklearn Algorithm Cheatsheet.mp4104.05MB

89 - 16.6 Recommendations.mp447.75MB