Description
Applied Time Series Analysis in Python course. This is the only course that combines the latest statistical and deep learning techniques for time series analysis. First, this course covers basic time series concepts:
- Stationarity and Augmented Dicker-Fuller Test
- being seasonal
- white noise
- Random walk
- Autoregression
- Moving Average
- ACF and PACF,
- Model selection with AIC (Akaike information criterion)
Then, we apply more sophisticated statistical models to time series forecasting:
- ARIMA (autoregressive integrated moving average model)
- SARIMA (Seasonal Autoregressive Integrated Moving Average Model)
- SARIMAX (integrated moving average model of seasonal regression with exogenous variables)
We also cover multiple time series forecasting with:
- VAR (vector autoregression)
- VARMA (vector autoregressive moving average model)
- VARMAX (vector autoregressive moving average model with exogenous variable)
Next, we move on to the deep learning section, where we use Tensorflow to apply various deep learning techniques to time series analysis:
- Simple linear model (1-layer neural network)
- DNN (Deep Neural Network)
- CNN (Convolutional Neural Network)
- LSTM (long short term memory)
- CNN + LSTM models
- ResNet (residual networks)
- LSTM autoregression
What you will learn in the Applied Time Series Analysis in Python course
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Descriptive statistics versus inferential statistics
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Random walk model
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Moving average model
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Arima, Sarima, Sarimax
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Use deep learning to analyze time series with TensorFlow
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Linear models, DNN, LSTM, CNN, ResNet
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Automate time series analysis with Prophet
This course is suitable for people who
- Beginning data scientists looking to gain experience with time series
- Deep learning beginners curious about time series
- Professional data scientists who need time series analysis
- Data scientists looking to migrate from R to Python
Specifications of Applied Time Series Analysis in Python course
- Publisher: Udemy
- teacher: Marco Peixeiro
- Training level: beginner to advanced
- Training duration: 6 hours and 5 minutes
- Number of courses: 40
Course topics on 7/2022
Prerequisites of the Applied Time Series Analysis in Python course
- Basic knowledge of Python
- Basic knowledge of deep learning
- Jupyter notebook installed (or access to Google Colab)
Course images
Sample video of the course
Installation guide
After Extract, view with your favorite Player.
English subtitle
Quality: 720p
download link
File(s) password: www.downloadly.ir
Size
1.5 GB
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