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[FreeCoursesOnline.Me] O'REILLY - Python for Data Science Complete Video Course Video Training
- Date: 2026-07-01
- Size: 13 GB
- Files: 95
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01 - Python for Data Science Complete Video Course Video Training - Introduction.mp4
77 MB
02 - Learning objectives.mp4
11 MB
03 - 1.1 History of Python in data science.mp4
78 MB
04 - 1.2 Overview of Python data science libraries.mp4
44 MB
05 - 1.3 Future trends of Python in AI, ML, and data science.mp4
78 MB
06 - Learning objectives.mp4
25 MB
07 - 2.1 Create your first Colab document.mp4
329 MB
08 - 2.2 Manage Colab documents.mp4
452 MB
09 - 2.3 Use magic functions.mp4
156 MB
10 - 2.4 Understand compatibility with Jupyter.mp4
258 MB
11 - Learning objectives.mp4
29 MB
12 - 3.1 Write procedural code.mp4
113 MB
13 - 3.2 Use simple expressions and variables.mp4
174 MB
14 - 3.3 Work with the built-in types.mp4
67 MB
15 - 3.4 Learn to Print.mp4
71 MB
16 - 3.5 Perform basic math operations.mp4
167 MB
17 - 3.6 Use classes and objects with dot notation.mp4
194 MB
18 - Learning objectives.mp4
17 MB
19 - 4.1 Use string methods.mp4
132 MB
20 - 4.2 Format strings.mp4
99 MB
21 - 4.3 Manipulate strings - membership, slicing, and concatenation.mp4
137 MB
22 - 4.4 Learn to use unicode.mp4
74 MB
23 - Learning objectives.mp4
22 MB
24 - 5.1 Use lists and tuples.mp4
370 MB
25 - 5.2 Explore dictionaries.mp4
213 MB
26 - 5.3 Dive into sets.mp4
83 MB
27 - 5.4 Work with the numpy array.mp4
234 MB
28 - 5.5 Use the Pandas DataFrame.mp4
117 MB
29 - 5.6 Use the Pandas Series.mp4
72 MB
30 - Learning objectives.mp4
24 MB
31 - 6.1 Convert lists to dicts and back.mp4
74 MB
32 - 6.2 Convert dicts to Pandas Dataframe.mp4
105 MB
33 - 6.3 Convert characters to integers and back.mp4
36 MB
34 - 6.4 Convert between hexadecimal, binary, and floats.mp4
101 MB
35 - Learning objectives.mp4
25 MB
36 - 7.1 Learn to loop with for loops.mp4
45 MB
37 - 7.2 Repeat with while loops.mp4
50 MB
38 - 7.3 Learn to handle exceptions.mp4
112 MB
39 - 7.4 Use conditionals.mp4
168 MB
40 - Learning objectives.mp4
22 MB
41 - 8.1 Write and use functions.mp4
206 MB
42 - 8.2 Learn to use decorators.mp4
211 MB
43 - 8.3 Compose closure functions.mp4
133 MB
44 - 8.4 Use lambdas.mp4
106 MB
45 - 8.5 Advanced Use of Functions.mp4
319 MB
46 - Learning objectives.mp4
34 MB
47 - 9.1 Learn NumPy.mp4
288 MB
48 - 9.2 Learn SciPy.mp4
665 MB
49 - 9.3 Learn Pandas.mp4
336 MB
50 - 9.4 Learn TensorFlow.mp4
342 MB
51 - 9.5 Use Seaborn for 2D plots.mp4
262 MB
52 - 9.6 Use Plotly for interactive plots.mp4
262 MB
53 - 9.7 Specialized Visualization Libraries.mp4
242 MB
54 - 9.8 Learn Natural Language Processing Libraries.mp4
125 MB
55 - Learning objectives.mp4
28 MB
56 - 10.1 Understand functional programming.mp4
151 MB
57 - 10.2 Apply functions to data science workflows.mp4
47 MB
58 - 10.3 Use map_reduce_filter.mp4
95 MB
59 - 10.4 Use list comprehensions.mp4
98 MB
60 - 10.5 Use dictionary comprehensions.mp4
15 MB
61 - Learning objectives.mp4
18 MB
62 - 11.1 Use generators.mp4
69 MB
63 - 11.2 Design generator pipelines.mp4
141 MB
64 - 11.3 Implement lazy evaluation functions.mp4
59 MB
65 - Learning objectives.mp4
21 MB
66 - 12.1 Perform simple pattern matching.mp4
97 MB
67 - 12.2 Use regular expressions.mp4
285 MB
68 - 12.3 Learn text processing techniques - Beautiful Soup.mp4
88 MB
69 - Learning objectives.mp4
18 MB
70 - 13.1 Sort in Python.mp4
187 MB
71 - 13.2 Create custom sorting functions.mp4
229 MB
72 - 13.3 Sort in Pandas.mp4
302 MB
73 - Learning objectives.mp4
22 MB
74 - 14.1 Read and write files - file, pickle, CSV, JSON.mp4
215 MB
75 - 14.2 Read and write with Pandas - CSV, JSON.mp4
336 MB
76 - 14.3 Read and write using web resources (requests, boto, github).mp4
111 MB
77 - 14.4 Use function-based concurrency.mp4
608 MB
78 - Learning objectives.mp4
21 MB
79 - 15.1 Share with Github.mp4
358 MB
80 - 15.2 Create Kaggle Kernels.mp4
208 MB
81 - 15.3 Collaborate with Colab.mp4
125 MB
82 - 15.4 Post public graphs with Plotly.mp4
104 MB
83 - Learning Objectives.mp4
29 MB
84 - 16.1 PyTest.mp4
373 MB
85 - 16.2 Visual Studio Code.mp4
365 MB
86 - 16.3 Vim.mp4
137 MB
87 - 16.4 Ludwig (Open Source AutoML).mp4
146 MB
88 - 16.5 Sklearn Algorithm Cheatsheet.mp4
104 MB
89 - 16.6 Recommendations.mp4
48 MB