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3.98 GB | 00:17:17 | mp4 | 1280X720 | 16:9
Genre: eLearning | Language : English
Files Included :
001 Course Curriculum (62.14 MB)
002 Course Overview and Aim (55.28 MB)
003 Knowledge Requirements (16.9 MB)
004 Course Material (5.23 MB)
001 Time series forecasting (49.6 MB)
002 Forecasting models (27.7 MB)
003 Datasets, features and targets (33.67 MB)
004 Forecasting framework (15.81 MB)
005 Feature engineering overview (26.57 MB)
006 Forecasting demo data analysis (70.94 MB)
007 Forecasting demo feature engineering (95.7 MB)
008 Forecasting demo training the forecaster (35.14 MB)
009 Summary (16.49 MB)
001 Challenges in feature engineering (20.58 MB)
002 Machine learning workflow (8.9 MB)
003 Feature engineering in tabular data (20.5 MB)
004 Feature engineering in forecasting - considerations (37.02 MB)
005 Feature engineering in forecasting - pipelines (8.95 MB)
006 Forecasting demo - intro (3.98 MB)
007 Feature engineering pipeline - demo (44.53 MB)
008 Forecasting one step ahead demo (32.79 MB)
009 Multistep forecasting (7.78 MB)
010 Direct forecasting (10.84 MB)
011 Direct multistep forecasting demo (66.86 MB)
012 Recursive forecasting (11.39 MB)
013 Recursive multistep forecasting demo (74.45 MB)
014 Recursive forecasting multiple horizons - demo (27.82 MB)
015 Summary (5.68 MB)
001 Components of a time series (16.45 MB)
002 White noise (19.98 MB)
003 Additive and multiplicative models (13.53 MB)
004 Log transform (9.25 MB)
005 Box-Cox transform (24.88 MB)
006 Box-Cox transform Guerrero method (23.6 MB)
007 Box-Cox using Scipy demo (43.03 MB)
008 Box-Cox using sktime and Feature-engine demo (26.34 MB)
009 Moving average (34.69 MB)
010 Moving averages in Pandas demo (32.64 MB)
011 Classical decomposition trend (16.07 MB)
012 Classical decomposition seasonality (25.46 MB)
013 Classical decomposition demo (29.88 MB)
014 LOWESS Theory (41.11 MB)
015 LOWESS Practice (15.42 MB)
016 LOWESS to extract trend demo (60.76 MB)
017 LOWESS vs LOESS (16.24 MB)
018 STL overview (33.22 MB)
019 STL theory part 1 LOESS and cycle-subseries (16.59 MB)
020 STL theory part 2 the inner loop (35.26 MB)
021 STL theory part 3 the outer loop (9.1 MB)
022 STL to compute seasonality and trend demo (60.78 MB)
023 Summary (55.03 MB)
001 Lag features (27.11 MB)
002 Lag features demo (24.2 MB)
003 How to choose the lags (28.03 MB)
004 Autoregressive (AR) processes (52.6 MB)
005 Lag plots (27.27 MB)
006 Lag plots demo (46.83 MB)
007 Autocorrelation function (part 1) (31.19 MB)
008 Autocorrelation function (part 2) (30.4 MB)
009 Autocorrelation function demo (55.55 MB)
010 Partial autocorrelation function (part 1) (19.32 MB)
011 Partial autocorrelation function (part 2) (30.46 MB)
012 Partial autocorrelation function demo (75.82 MB)
013 Cross correlation function (part 1) (14.99 MB)
014 Cross correlation function (part 2) (45.83 MB)
015 Cross correlation function demo (95.78 MB)
016 Creating good lag features demo the air pollution dataset (40.96 MB)
017 Creating good lag features demo domain knowledge (74.25 MB)
018 Creating good lag features demo feature selection & modelling (70.04 MB)
019 Creating good lag features demo correlation methods (part 1) (54.8 MB)
020 Creating good lag features demo correlation methods (part 2) (72.24 MB)
021 Summary (20.93 MB)
001 Window features overview (5.43 MB)
002 Rolling window features definition (9.68 MB)
003 Rolling window features picking the window size and statistics (26.21 MB)
004 Rolling window features implementation in Python (20.94 MB)
005 Rolling window features demo (84.47 MB)
006 Expanding window features definition (4.84 MB)
007 Expanding window features use cases (7.83 MB)
008 Expanding window features implementation in Python (7.77 MB)
009 Expanding window features demo (26.1 MB)
010 Weighted window functions definition & use cases (50.95 MB)
011 Weighted window functions implementation in Python (12.25 MB)
012 Weighted window functions demo (93.41 MB)
013 Exponential weights definition (9.82 MB)
014 Exponential weights expanding windows and implementation (12.92 MB)
015 Exponential weights demo (102.05 MB)
016 Summary (24.88 MB)
001 Trend features overview (8.6 MB)
002 Types of trend (14.67 MB)
003 Linear trend using time as a feature (49.71 MB)
004 Time feature creating the feature demo (60.28 MB)
005 Time feature forecasting demo (104.05 MB)
006 Non-linear trend using time as a feature (22.68 MB)
007 Non-linear time features creating the features demo (12.91 MB)
008 Non-linear time features forecasting demo (49.65 MB)
009 Recursive forecasting with lags, windows, and trend (17.22 MB)
010 Trend features and recursive forecasting demo (102.04 MB)
011 Piecewise regression and changepoints (part 1) (17.42 MB)
012 Piecewise regression and changepoints (part 2) (17.02 MB)
013 Changepoint features creating the features demo (41.61 MB)
014 Changepoint features forecasting demo (64.52 MB)
015 Summary (12.45 MB)
001 Seasonality and cyclical patterns overview (24.45 MB)
002 Seasonal lag features (15.69 MB)
003 Seasonal lag features demo (93.05 MB)
004 Date and time features for seasonality (8.93 MB)
005 Why linear models struggle with date and time features (9.8 MB)
006 Date and time features demo (part 1) (49.78 MB)
007 Date and time features demo (part 2) (49.44 MB)
008 Summary (8.85 MB)
001 Date and time features (7.54 MB)
002 Date features demo (123.41 MB)
003 Time features demo (24.64 MB)
004 Periodic or Cyclical Features (13.98 MB)
005 Periodic Features demo (57.62 MB)
006 Summary (2.05 MB)]
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