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[CourseClub Me] O'REILLY - Python for Data Science Complete Video Course Video Training
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Torrent Files List
01 - Python for Data Science Complete Video Course Video Training - Introduction.mp4 (Size: 13.13 GB) (Files: 91)
01 - Python for Data Science Complete Video Course Video Training - Introduction.mp4
02 - Learning objectives.mp4
03 - 1.1 History of Python in data science.mp4
04 - 1.2 Overview of Python data science libraries.mp4
05 - 1.3 Future trends of Python in AI, ML, and data science.mp4
06 - Learning objectives.mp4
07 - 2.1 Create your first Colab document.mp4
08 - 2.2 Manage Colab documents.mp4
09 - 2.3 Use magic functions.mp4
10 - 2.4 Understand compatibility with Jupyter.mp4
11 - Learning objectives.mp4
12 - 3.1 Write procedural code.mp4
13 - 3.2 Use simple expressions and variables.mp4
14 - 3.3 Work with the built-in types.mp4
15 - 3.4 Learn to Print.mp4
16 - 3.5 Perform basic math operations.mp4
17 - 3.6 Use classes and objects with dot notation.mp4
18 - Learning objectives.mp4
19 - 4.1 Use string methods.mp4
20 - 4.2 Format strings.mp4
21 - 4.3 Manipulate strings - membership, slicing, and concatenation.mp4
22 - 4.4 Learn to use unicode.mp4
23 - Learning objectives.mp4
24 - 5.1 Use lists and tuples.mp4
25 - 5.2 Explore dictionaries.mp4
26 - 5.3 Dive into sets.mp4
27 - 5.4 Work with the numpy array.mp4
28 - 5.5 Use the Pandas DataFrame.mp4
29 - 5.6 Use the Pandas Series.mp4
30 - Learning objectives.mp4
31 - 6.1 Convert lists to dicts and back.mp4
32 - 6.2 Convert dicts to Pandas Dataframe.mp4
33 - 6.3 Convert characters to integers and back.mp4
34 - 6.4 Convert between hexadecimal, binary, and floats.mp4
35 - Learning objectives.mp4
36 - 7.1 Learn to loop with for loops.mp4
37 - 7.2 Repeat with while loops.mp4
38 - 7.3 Learn to handle exceptions.mp4
39 - 7.4 Use conditionals.mp4
40 - Learning objectives.mp4
41 - 8.1 Write and use functions.mp4
42 - 8.2 Learn to use decorators.mp4
43 - 8.3 Compose closure functions.mp4
44 - 8.4 Use lambdas.mp4
45 - 8.5 Advanced Use of Functions.mp4
46 - Learning objectives.mp4
47 - 9.1 Learn NumPy.mp4
48 - 9.2 Learn SciPy.mp4
49 - 9.3 Learn Pandas.mp4
50 - 9.4 Learn TensorFlow.mp4
51 - 9.5 Use Seaborn for 2D plots.mp4
52 - 9.6 Use Plotly for interactive plots.mp4
53 - 9.7 Specialized Visualization Libraries.mp4
54 - 9.8 Learn Natural Language Processing Libraries.mp4
55 - Learning objectives.mp4
56 - 10.1 Understand functional programming.mp4
57 - 10.2 Apply functions to data science workflows.mp4
58 - 10.3 Use map_reduce_filter.mp4
59 - 10.4 Use list comprehensions.mp4
60 - 10.5 Use dictionary comprehensions.mp4
61 - Learning objectives.mp4
62 - 11.1 Use generators.mp4
63 - 11.2 Design generator pipelines.mp4
64 - 11.3 Implement lazy evaluation functions.mp4
65 - Learning objectives.mp4
66 - 12.1 Perform simple pattern matching.mp4
67 - 12.2 Use regular expressions.mp4
68 - 12.3 Learn text processing techniques - Beautiful Soup.mp4
69 - Learning objectives.mp4
70 - 13.1 Sort in Python.mp4
71 - 13.2 Create custom sorting functions.mp4
72 - 13.3 Sort in Pandas.mp4
73 - Learning objectives.mp4
74 - 14.1 Read and write files - file, pickle, CSV, JSON.mp4
75 - 14.2 Read and write with Pandas - CSV, JSON.mp4
76 - 14.3 Read and write using web resources (requests, boto, github).mp4
77 - 14.4 Use function-based concurrency.mp4
78 - Learning objectives.mp4
79 - 15.1 Share with Github.mp4
80 - 15.2 Create Kaggle Kernels.mp4
81 - 15.3 Collaborate with Colab.mp4
82 - 15.4 Post public graphs with Plotly.mp4
83 - Learning Objectives.mp4
84 - 16.1 PyTest.mp4
85 - 16.2 Visual Studio Code.mp4
86 - 16.3 Vim.mp4
87 - 16.4 Ludwig (Open Source AutoML).mp4
88 - 16.5 Sklearn Algorithm Cheatsheet.mp4
89 - 16.6 Recommendations.mp4
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