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GetFreeCourses Co-Udemy-Time Series Analysis, Forecasting, and Machine Learning
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1. Welcome (Size: 6.82 GB) (Files: 352)
1. Welcome
1. Introduction and Outline.mp4
1. Introduction and Outline.srt
2. Warmup (Optional).mp4
2. Warmup (Optional).srt
10. Deep Learning Recurrent Neural Networks (RNN)
1. RNN Section Introduction.mp4
1. RNN Section Introduction.srt
10. LSTMs for Time Series Classification in Code.mp4
10. LSTMs for Time Series Classification in Code.srt
11. The Unreasonable Ineffectiveness of Recurrent Neural Networks.mp4
11. The Unreasonable Ineffectiveness of Recurrent Neural Networks.srt
12. RNN Section Summary.mp4
12. RNN Section Summary.srt
2. Simple RNN Elman Unit (pt 1).mp4
2. Simple RNN Elman Unit (pt 1).srt
3. Simple RNN Elman Unit (pt 2).mp4
3. Simple RNN Elman Unit (pt 2).srt
4. Aside State Space Models vs. RNNs.mp4
4. Aside State Space Models vs. RNNs.srt
5. RNN Code Preparation.mp4
5. RNN Code Preparation.srt
6. RNNs Understanding by Implementing (Paying Attention to Shapes).mp4
6. RNNs Understanding by Implementing (Paying Attention to Shapes).srt
7. GRU and LSTM (pt 1).mp4
7. GRU and LSTM (pt 1).srt
8. GRU and LSTM (pt 2).mp4
8. GRU and LSTM (pt 2).srt
9. LSTMs for Time Series Forecasting in Code.mp4
9. LSTMs for Time Series Forecasting in Code.srt
11. VIP GARCH
1. GARCH Section Introduction.mp4
1. GARCH Section Introduction.srt
10. GARCH Code (pt 3).mp4
10. GARCH Code (pt 3).srt
11. GARCH Code (pt 4).mp4
11. GARCH Code (pt 4).srt
12. GARCH Code (pt 5).mp4
12. GARCH Code (pt 5).srt
13. A Deep Learning Approach to GARCH.mp4
13. A Deep Learning Approach to GARCH.srt
14. GARCH Section Summary.mp4
14. GARCH Section Summary.srt
2. ARCH Theory (pt 1).mp4
2. ARCH Theory (pt 1).srt
3. ARCH Theory (pt 2).mp4
3. ARCH Theory (pt 2).srt
4. ARCH Theory (pt 3).mp4
4. ARCH Theory (pt 3).srt
5. GARCH Theory.mp4
5. GARCH Theory.srt
6. GARCH Code Preparation (pt 1).mp4
6. GARCH Code Preparation (pt 1).srt
7. GARCH Code Preparation (pt 2).mp4
7. GARCH Code Preparation (pt 2).srt
8. GARCH Code (pt 1).mp4
8. GARCH Code (pt 1).srt
9. GARCH Code (pt 2).mp4
9. GARCH Code (pt 2).srt
12. VIP AWS Forecast
1. AWS Forecast Section Introduction.mp4
1. AWS Forecast Section Introduction.srt
2. Data Model.mp4
2. Data Model.srt
3. Creating an IAM Role.mp4
3. Creating an IAM Role.srt
4. Code pt 1 (Getting and Transforming the Data).mp4
4. Code pt 1 (Getting and Transforming the Data).srt
5. Code pt 2 (Uploading the data to S3).mp4
5. Code pt 2 (Uploading the data to S3).srt
6. Code pt 3 (Building your Model).mp4
6. Code pt 3 (Building your Model).srt
7. Code pt 4 (Generating and Evaluating the Forecast).mp4
7. Code pt 4 (Generating and Evaluating the Forecast).srt
8. AWS Forecast Exercise.mp4
8. AWS Forecast Exercise.srt
9. AWS Forecast Section Summary.mp4
9. AWS Forecast Section Summary.srt
13. VIP Facebook Prophet
1. Prophet Section Introduction.mp4
1. Prophet Section Introduction.srt
10. (The Dangers of) Prophet for Stock Price Prediction.mp4
10. (The Dangers of) Prophet for Stock Price Prediction.srt
11. Prophet Section Summary.mp4
11. Prophet Section Summary.srt
2. How does Prophet work.mp4
2. How does Prophet work.srt
3. Prophet Code Preparation.mp4
3. Prophet Code Preparation.srt
4. Prophet in Code Data Preparation.mp4
4. Prophet in Code Data Preparation.srt
5. Prophet in Code Fit, Forecast, Plot.mp4
5. Prophet in Code Fit, Forecast, Plot.srt
6. Prophet in Code Holidays and Exogenous Regressors.mp4
6. Prophet in Code Holidays and Exogenous Regressors.srt
7. Prophet in Code Cross-Validation.mp4
7. Prophet in Code Cross-Validation.srt
8. Prophet in Code Changepoint Detection.mp4
8. Prophet in Code Changepoint Detection.srt
9. Prophet Multiplicative Seasonality, Outliers, Non-Daily Data.mp4
9. Prophet Multiplicative Seasonality, Outliers, Non-Daily Data.srt
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14. Setting Up Your Environment FAQ
1. Anaconda Environment Setup.mp4
1. Anaconda Environment Setup.srt
2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4
2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt
15. Extra Help With Python Coding for Beginners FAQ
1. How to Code by Yourself (part 1).mp4
1. How to Code by Yourself (part 1).srt
2. How to Code by Yourself (part 2).mp4
2. How to Code by Yourself (part 2).srt
3. Proof that using Jupyter Notebook is the same as not using it.mp4
3. Proof that using Jupyter Notebook is the same as not using it.srt
16. Effective Learning Strategies for Machine Learning FAQ
1. How to Succeed in this Course (Long Version).mp4
1. How to Succeed in this Course (Long Version).srt
2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4
2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt
3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4
3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt
4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4
4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt
17. Appendix FAQ Finale
1. What is the Appendix.mp4
1. What is the Appendix.srt
2. BONUS Lecture.mp4
2. BONUS Lecture.srt
2. Getting Set Up
1. Where to Get the Code.mp4
1. Where to Get the Code.srt
1.1 Data Links.html
1.2 Github Link.html
2. How to use Github & Extra Coding Tips (Optional).mp4
2. How to use Github & Extra Coding Tips (Optional).srt
3. Time Series Basics
1. Time Series Basics Section Introduction.mp4
1. Time Series Basics Section Introduction.srt
10. Price Simulations in Code.mp4
10. Price Simulations in Code.srt
11. Random Walks and the Random Walk Hypothesis.mp4
11. Random Walks and the Random Walk Hypothesis.srt
12. The Naive Forecast and the Importance of Baselines.mp4
12. The Naive Forecast and the Importance of Baselines.srt
13. Naive Forecast and Forecasting Metrics in Code.mp4
13. Naive Forecast and Forecasting Metrics in Code.srt
14. Time Series Basics Section Summary.mp4
14. Time Series Basics Section Summary.srt
15. Suggestion Box.mp4
15. Suggestion Box.srt
2. What is a Time Series.mp4
2. What is a Time Series.srt
3. Modeling vs. Predicting.mp4
3. Modeling vs. Predicting.srt
4. Why Do We Care About Shapes.mp4
4. Why Do We Care About Shapes.srt
5. Types of Tasks.mp4
5. Types of Tasks.srt
6. Power, Log, and Box-Cox Transformations.mp4
6. Power, Log, and Box-Cox Transformations.srt
7. Power, Log, and Box-Cox Transformations in Code.mp4
7. Power, Log, and Box-Cox Transformations in Code.srt
8. Forecasting Metrics.mp4
8. Forecasting Metrics.srt
9. Financial Time Series Primer.mp4
9. Financial Time Series Primer.srt
4. Exponential Smoothing and ETS Methods
1. Exponential Smoothing Section Introduction.mp4
1. Exponential Smoothing Section Introduction.srt
10. Holt's Linear Trend Model (Code).mp4
10. Holt's Linear Trend Model (Code).srt
11. Holt-Winters (Theory).mp4
11. Holt-Winters (Theory).srt
12. Holt-Winters (Code).mp4
12. Holt-Winters (Code).srt
13. Walk-Forward Validation.mp4
13. Walk-Forward Validation.srt
14. Walk-Forward Validation in Code.mp4
14. Walk-Forward Validation in Code.srt
15. Application Sales Data.mp4
15. Application Sales Data.srt
16. Application Stock Predictions.mp4
16. Application Stock Predictions.srt
17. SMA Application COVID-19 Counting.mp4
17. SMA Application COVID-19 Counting.srt
18. SMA Application Algorithmic Trading.mp4
18. SMA Application Algorithmic Trading.srt
19. Exponential Smoothing Section Summary.mp4
19. Exponential Smoothing Section Summary.srt
2. Exponential Smoothing Intuition for Beginners.mp4
2. Exponential Smoothing Intuition for Beginners.srt
20. (Optional) More About State-Space Models.mp4
20. (Optional) More About State-Space Models.srt
3. SMA Theory.mp4
3. SMA Theory.srt
4. SMA Code.mp4
4. SMA Code.srt
5. EWMA Theory.mp4
5. EWMA Theory.srt
6. EWMA Code.mp4
6. EWMA Code.srt
7. SES Theory.mp4
7. SES Theory.srt
8. SES Code.mp4
8. SES Code.srt
9. Holt's Linear Trend Model (Theory).mp4
9. Holt's Linear Trend Model (Theory).srt
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5. ARIMA
1. ARIMA Section Introduction.mp4
1. ARIMA Section Introduction.srt
10. ACF and PACF in Code (pt 1).mp4
10. ACF and PACF in Code (pt 1).srt
11. ACF and PACF in Code (pt 2).mp4
11. ACF and PACF in Code (pt 2).srt
12. Auto ARIMA and SARIMAX.mp4
12. Auto ARIMA and SARIMAX.srt
13. Model Selection, AIC and BIC.mp4
13. Model Selection, AIC and BIC.srt
14. Auto ARIMA in Code.mp4
14. Auto ARIMA in Code.srt
15. Auto ARIMA in Code (Stocks).mp4
15. Auto ARIMA in Code (Stocks).srt
16. ACF and PACF for Stock Returns.mp4
16. ACF and PACF for Stock Returns.srt
17. Auto ARIMA in Code (Sales Data).mp4
17. Auto ARIMA in Code (Sales Data).srt
18. How to Forecast with ARIMA.mp4
18. How to Forecast with ARIMA.srt
19. Forecasting Out-Of-Sample.mp4
19. Forecasting Out-Of-Sample.srt
2. Autoregressive Models - AR(p).mp4
2. Autoregressive Models - AR(p).srt
20. ARIMA Section Summary.mp4
20. ARIMA Section Summary.srt
3. Moving Average Models - MA(q).mp4
3. Moving Average Models - MA(q).srt
4. ARIMA.mp4
4. ARIMA.srt
5. ARIMA in Code.mp4
5. ARIMA in Code.srt
6. Stationarity.mp4
6. Stationarity.srt
7. Stationarity in Code.mp4
7. Stationarity in Code.srt
8. ACF (Autocorrelation Function).mp4
8. ACF (Autocorrelation Function).srt
9. PACF (Partial Autocorrelation Funtion).mp4
9. PACF (Partial Autocorrelation Funtion).srt
6. Vector Autoregression (VAR, VMA, VARMA)
1. Vector Autoregression Section Introduction.mp4
1. Vector Autoregression Section Introduction.srt
10. Converting Between Models (Optional).mp4
10. Converting Between Models (Optional).srt
11. Vector Autoregression Section Summary.mp4
11. Vector Autoregression Section Summary.srt
2. VAR and VARMA Theory.mp4
2. VAR and VARMA Theory.srt
3. VARMA Code (pt 1).mp4
3. VARMA Code (pt 1).srt
4. VARMA Code (pt 2).mp4
4. VARMA Code (pt 2).srt
5. VARMA Code (pt 3).mp4
5. VARMA Code (pt 3).srt
6. VARMA Econometrics Code (pt 1).mp4
6. VARMA Econometrics Code (pt 1).srt
7. VARMA Econometrics Code (pt 2).mp4
7. VARMA Econometrics Code (pt 2).srt
8. Granger Causality.mp4
8. Granger Causality.srt
9. Granger Causality Code.mp4
9. Granger Causality Code.srt
7. Machine Learning Methods
1. Machine Learning Section Introduction.mp4
1. Machine Learning Section Introduction.srt
10. Forecasting with Differencing.mp4
10. Forecasting with Differencing.srt
11. Machine Learning for Time Series Forecasting in Code (pt 2).mp4
11. Machine Learning for Time Series Forecasting in Code (pt 2).srt
12. Application Sales Data.mp4
12. Application Sales Data.srt
13. Application Predicting Stock Prices and Returns.mp4
13. Application Predicting Stock Prices and Returns.srt
14. Application Predicting Stock Movements.mp4
14. Application Predicting Stock Movements.srt
15. Machine Learning Section Summary.mp4
15. Machine Learning Section Summary.srt
2. Supervised Machine Learning Classification and Regression.mp4
2. Supervised Machine Learning Classification and Regression.srt
3. Autoregressive Machine Learning Models.mp4
3. Autoregressive Machine Learning Models.srt
4. Machine Learning Algorithms Linear Regression.mp4
4. Machine Learning Algorithms Linear Regression.srt
5. Machine Learning Algorithms Logistic Regression.mp4
5. Machine Learning Algorithms Logistic Regression.srt
6. Machine Learning Algorithms Support Vector Machines.mp4
6. Machine Learning Algorithms Support Vector Machines.srt
7. Machine Learning Algorithms Random Forest.mp4
7. Machine Learning Algorithms Random Forest.srt
8. Extrapolation and Stock Prices.mp4
8. Extrapolation and Stock Prices.srt
9. Machine Learning for Time Series Forecasting in Code (pt 1).mp4
9. Machine Learning for Time Series Forecasting in Code (pt 1).srt
8. Deep Learning Artificial Neural Networks (ANN)
1. Artificial Neural Networks Section Introduction.mp4
1. Artificial Neural Networks Section Introduction.srt
10. Human Activity Recognition Dataset.mp4
10. Human Activity Recognition Dataset.srt
11. Human Activity Recognition Code Preparation.mp4
11. Human Activity Recognition Code Preparation.srt
12. Human Activity Recognition Data Exploration.mp4
12. Human Activity Recognition Data Exploration.srt
13. Human Activity Recognition Multi-Input ANN.mp4
13. Human Activity Recognition Multi-Input ANN.srt
14. Human Activity Recognition Feature-Based Model.mp4
14. Human Activity Recognition Feature-Based Model.srt
15. Human Activity Recognition Combined Model.mp4
15. Human Activity Recognition Combined Model.srt
16. How Does a Neural Network Learn.mp4
16. How Does a Neural Network Learn.srt
17. Artificial Neural Networks Section Summary.mp4
17. Artificial Neural Networks Section Summary.srt
2. The Neuron.mp4
2. The Neuron.srt
3. Forward Propagation.mp4
3. Forward Propagation.srt
4. The Geometrical Picture.mp4
4. The Geometrical Picture.srt
5. Activation Functions.mp4
5. Activation Functions.srt
6. Multiclass Classification.mp4
6. Multiclass Classification.srt
7. ANN Code Preparation.mp4
7. ANN Code Preparation.srt
8. Feedforward ANN for Time Series Forecasting Code.mp4
8. Feedforward ANN for Time Series Forecasting Code.srt
9. Feedforward ANN for Stock Return and Price Predictions Code.mp4
9. Feedforward ANN for Stock Return and Price Predictions Code.srt
9. Deep Learning Convolutional Neural Networks (CNN)
1. CNN Section Introduction.mp4
1. CNN Section Introduction.srt
10. CNN for Human Activity Recognition.mp4
10. CNN for Human Activity Recognition.srt
11. CNN Section Summary.mp4
11. CNN Section Summary.srt
11.1 Convert a Time Series Into an Image with Gramian Angular Fields and Markov Transition Fields.html
2. What is Convolution.mp4
2. What is Convolution.srt
3. What is Convolution (Pattern-Matching).mp4
3. What is Convolution (Pattern-Matching).srt
4. What is Convolution (Weight Sharing).mp4
4. What is Convolution (Weight Sharing).srt
5. Convolution on Color Images.mp4
5. Convolution on Color Images.srt
6. Convolution for Time Series and ARIMA.mp4
6. Convolution for Time Series and ARIMA.srt
7. CNN Architecture.mp4
7. CNN Architecture.srt
8. CNN Code Preparation.mp4
8. CNN Code Preparation.srt
9. CNN for Time Series Forecasting in Code.mp4
9. CNN for Time Series Forecasting in Code.srt
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