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01 - Python for Data Science Complete Video Course Video Training - Introduction.mp4
76.64MB
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02 - Learning objectives.mp4
11.21MB
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03 - 1.1 History of Python in data science.mp4
78.08MB
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04 - 1.2 Overview of Python data science libraries.mp4
44.37MB
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05 - 1.3 Future trends of Python in AI, ML, and data science.mp4
77.54MB
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06 - Learning objectives.mp4
25MB
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07 - 2.1 Create your first Colab document.mp4
328.82MB
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08 - 2.2 Manage Colab documents.mp4
451.8MB
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09 - 2.3 Use magic functions.mp4
156.26MB
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10 - 2.4 Understand compatibility with Jupyter.mp4
258.05MB
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11 - Learning objectives.mp4
28.81MB
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12 - 3.1 Write procedural code.mp4
112.86MB
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13 - 3.2 Use simple expressions and variables.mp4
173.93MB
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14 - 3.3 Work with the built-in types.mp4
66.6MB
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15 - 3.4 Learn to Print.mp4
70.6MB
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16 - 3.5 Perform basic math operations.mp4
167.11MB
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17 - 3.6 Use classes and objects with dot notation.mp4
194.46MB
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18 - Learning objectives.mp4
17MB
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19 - 4.1 Use string methods.mp4
131.93MB
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20 - 4.2 Format strings.mp4
98.69MB
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21 - 4.3 Manipulate strings - membership, slicing, and concatenation.mp4
136.75MB
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22 - 4.4 Learn to use unicode.mp4
74.37MB
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23 - Learning objectives.mp4
22.45MB
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24 - 5.1 Use lists and tuples.mp4
369.96MB
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25 - 5.2 Explore dictionaries.mp4
213.33MB
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26 - 5.3 Dive into sets.mp4
83.03MB
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27 - 5.4 Work with the numpy array.mp4
234.44MB
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28 - 5.5 Use the Pandas DataFrame.mp4
116.78MB
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29 - 5.6 Use the Pandas Series.mp4
71.62MB
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30 - Learning objectives.mp4
24MB
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31 - 6.1 Convert lists to dicts and back.mp4
74.45MB
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32 - 6.2 Convert dicts to Pandas Dataframe.mp4
104.57MB
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33 - 6.3 Convert characters to integers and back.mp4
35.73MB
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34 - 6.4 Convert between hexadecimal, binary, and floats.mp4
101.36MB
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35 - Learning objectives.mp4
24.93MB
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36 - 7.1 Learn to loop with for loops.mp4
44.92MB
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37 - 7.2 Repeat with while loops.mp4
50.23MB
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38 - 7.3 Learn to handle exceptions.mp4
111.94MB
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39 - 7.4 Use conditionals.mp4
168.25MB
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40 - Learning objectives.mp4
22.46MB
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41 - 8.1 Write and use functions.mp4
206.47MB
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42 - 8.2 Learn to use decorators.mp4
210.94MB
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43 - 8.3 Compose closure functions.mp4
132.86MB
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44 - 8.4 Use lambdas.mp4
106.23MB
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45 - 8.5 Advanced Use of Functions.mp4
319.02MB
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46 - Learning objectives.mp4
33.79MB
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47 - 9.1 Learn NumPy.mp4
287.95MB
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48 - 9.2 Learn SciPy.mp4
664.99MB
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49 - 9.3 Learn Pandas.mp4
335.61MB
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50 - 9.4 Learn TensorFlow.mp4
341.9MB
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51 - 9.5 Use Seaborn for 2D plots.mp4
261.65MB
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52 - 9.6 Use Plotly for interactive plots.mp4
262.06MB
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53 - 9.7 Specialized Visualization Libraries.mp4
241.69MB
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54 - 9.8 Learn Natural Language Processing Libraries.mp4
124.95MB
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55 - Learning objectives.mp4
27.7MB
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56 - 10.1 Understand functional programming.mp4
151.13MB
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57 - 10.2 Apply functions to data science workflows.mp4
47.12MB
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58 - 10.3 Use map_reduce_filter.mp4
95.23MB
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59 - 10.4 Use list comprehensions.mp4
98.27MB
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60 - 10.5 Use dictionary comprehensions.mp4
15.45MB
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61 - Learning objectives.mp4
17.83MB
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62 - 11.1 Use generators.mp4
69.4MB
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63 - 11.2 Design generator pipelines.mp4
141.25MB
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64 - 11.3 Implement lazy evaluation functions.mp4
59.14MB
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65 - Learning objectives.mp4
20.97MB
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66 - 12.1 Perform simple pattern matching.mp4
97.05MB
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67 - 12.2 Use regular expressions.mp4
284.59MB
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68 - 12.3 Learn text processing techniques - Beautiful Soup.mp4
87.6MB
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69 - Learning objectives.mp4
18.2MB
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70 - 13.1 Sort in Python.mp4
186.66MB
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71 - 13.2 Create custom sorting functions.mp4
229.33MB
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72 - 13.3 Sort in Pandas.mp4
301.95MB
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73 - Learning objectives.mp4
22.1MB
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74 - 14.1 Read and write files - file, pickle, CSV, JSON.mp4
214.71MB
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75 - 14.2 Read and write with Pandas - CSV, JSON.mp4
336.5MB
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76 - 14.3 Read and write using web resources (requests, boto, github).mp4
110.86MB
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77 - 14.4 Use function-based concurrency.mp4
608.14MB
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78 - Learning objectives.mp4
20.91MB
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79 - 15.1 Share with Github.mp4
358.09MB
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80 - 15.2 Create Kaggle Kernels.mp4
207.48MB
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81 - 15.3 Collaborate with Colab.mp4
125.18MB
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82 - 15.4 Post public graphs with Plotly.mp4
103.5MB
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83 - Learning Objectives.mp4
28.71MB
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84 - 16.1 PyTest.mp4
372.92MB
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85 - 16.2 Visual Studio Code.mp4
364.64MB
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86 - 16.3 Vim.mp4
136.81MB
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87 - 16.4 Ludwig (Open Source AutoML).mp4
146.48MB
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88 - 16.5 Sklearn Algorithm Cheatsheet.mp4
104.05MB
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89 - 16.6 Recommendations.mp4
47.75MB
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