UDEMY Time Series Analysis and Forecasting using Python BOOKWARE-iLEARN

By Pacman On Jun 1, 2022

UDEMY Time Series Analysis and Forecasting using Python BOOKWARE-iLEARN | Apps-Tutorials | MP4 | 4.34 GiB

968 kb/s 1280×720 | AAC 128 kb/s 2 CH

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23M	1. Welcome to the course.mp4
66M	11. Opening Jupyter Notebook.mp4
41M	12. Introduction to Jupyter.mp4
13M	13. Arithmetic operators in Python Python Basics.mp4
65M	14. Strings in Python Python Basics.mp4
61M	15. Lists, Tuples and Directories Python Basics.mp4
44M	16. Working with Numpy Library of Python.mp4
47M	17. Working with Pandas Library of Python.mp4
41M	18. Working with Seaborn Library of Python.mp4
109M	19. Data Loading in Python.mp4
13M	2. What is Time Series Forecasting.mp4
60M	20. Time Series - Feature Engineering Basics.mp4
113M	21. Time Series - Feature Engineering in Python.mp4
17M	22. Time Series - Upsampling and Downsampling.mp4
101M	23. Time Series - Upsampling and Downsampling in Python.mp4
64M	24. Time Series - Visualization Basics.mp4
166M	25. Time Series - Visualization in Python.mp4
15M	26. Time Series - Power Transformation.mp4
39M	27. Moving Average.mp4
8.4M	28. Exponential Smoothing.mp4
12M	29. White Noise.mp4
22M	30. Random Walk.mp4
60M	31. Decomposing Time Series in Python.mp4
33M	32. Differencing.mp4
113M	33. Differencing in Python.mp4
58M	34. Test Train Split in Python.mp4
44M	35. Naive (Persistence) model in Python.mp4
17M	36. Auto Regression Model - Basics.mp4
54M	37. Auto Regression Model creation in Python.mp4
50M	38. Auto Regression with Walk Forward validation in Python.mp4
25M	39. Moving Average model -Basics.mp4
21M	4. This is a milestone!.mp4
57M	40. Moving Average model in Python.mp4
42M	41. ACF and PACF.mp4
22M	42. ARIMA model - Basics.mp4
75M	43. ARIMA model in Python.mp4
33M	44. ARIMA model with Walk Forward Validation in Python.mp4
40M	45. SARIMA model.mp4
67M	46. SARIMA model in Python.mp4
5.6M	47. Stationary Time Series.mp4
9.3M	48. Introduction.mp4
26M	5. Time Series Forecasting - Use cases.mp4
15M	50. Gathering Business Knowledge.mp4
21M	51. Data Exploration.mp4
70M	52. The Dataset and the Data Dictionary.mp4
28M	53. Importing Data in Python.mp4
25M	54. Univariate analysis and EDD.mp4
62M	55. EDD in Python.mp4
25M	56. Outlier Treatment.mp4
71M	57. Outlier Treatment in Python.mp4
25M	58. Missing Value Imputation.mp4
24M	59. Missing Value Imputation in Python.mp4
11M	6. Forecasting model creation - Steps.mp4
18M	60. Seasonality in Data.mp4
101M	61. Bi-variate analysis and Variable transformation.mp4
45M	62. Variable transformation and deletion in Python.mp4
21M	63. Non-usable variables.mp4
37M	64. Dummy variable creation Handling qualitative data.mp4
27M	65. Dummy variable creation in Python.mp4
72M	66. Correlation Analysis.mp4
56M	67. Correlation Analysis in Python.mp4
9.4M	68. The Problem Statement.mp4
44M	69. Basic Equations and Ordinary Least Squares (OLS) method.mp4
35M	7. Forecasting model creation - Steps 1 (Goal).mp4
93M	70. Assessing accuracy of predicted coefficients.mp4
44M	71. Assessing Model Accuracy RSE and R squared.mp4
64M	72. Simple Linear Regression in Python.mp4
35M	73. Multiple Linear Regression.mp4
56M	74. The F - statistic.mp4
23M	75. Interpreting results of Categorical variables.mp4
70M	76. Multiple Linear Regression in Python.mp4
42M	77. Test-train split.mp4
26M	78. Bias Variance trade-off.mp4
63M	8. Time Series - Basic Notations.mp4
45M	80. Test train split in Python.mp4
30M	81. Introduction to Neural Networks and Course flow.mp4
45M	82. Perceptron.mp4
35M	83. Activation Functions.mp4
87M	84. Python - Creating Perceptron model.mp4
41M	85. Basic Terminologies.mp4
61M	86. Gradient Descent.mp4
123M	87. Back Propagation.mp4
63M	88. Some Important Concepts.mp4
46M	89. Hyperparameters.mp4
17M	9. Installing Python and Anaconda.mp4
15M	90. Keras and Tensorflow.mp4
21M	91. Installing Tensorflow and Keras.mp4
57M	92. Dataset for classification.mp4
45M	93. Normalization and Test-Train split.mp4
11M	94. Different ways to create ANN using Keras.mp4
80M	95. Building the Neural Network using Keras.mp4
82M	96. Compiling and Training the Neural Network model.mp4
70M	97. Evaluating performance and Predicting using Keras.mp4
156M	98. Building Neural Network for Regression Problem.mp4
12M	99. The final milestone!.mp4
4.4G	total

File: 1. Welcome to the course.mp4
Size: 23286599 bytes (22.21 MiB), duration: 00:03:12, avg.bitrate: 970 kb/s
Audio: aac, 44100 Hz, stereo (und)
Video: h264, yuv420p, 1280x720, 30.00 fps(r) => 1279x720 (und)

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Keywords: UDEMY, Time, Series, Analysis, and, Forecasting, using, Python, BOOKWARE, iLEARN

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