GetFreeCourses.Co-Udemy-Time Series Analysis, Forecasting, and Machine Learning

磁力链接/BT种子名称

GetFreeCourses.Co-Udemy-Time Series Analysis, Forecasting, and Machine Learning

磁力链接/BT种子简介

种子哈希：5a09a383a63c7f01e08784052833d37bc841544b
文件大小： 6.82G
已经下载：2651次
下载速度：极快
收录时间：2023-12-27
最近下载：2025-10-03

移花宫入口

移花宫.com邀月.com怜星.com花无缺.comyhgbt.icuyhgbt.top

磁力链接下载

magnet:?xt=urn:btih:5A09A383A63C7F01E08784052833D37BC841544B
推荐使用PIKPAK网盘下载资源，10TB超大空间，不限制资源，无限次数离线下载，视频在线观看

下载BT种子文件

磁力链接 迅雷下载 PIKPAK在线播放 世界之窗 91视频 含羞草 欲漫涩 逼哩逼哩 成人快手 51品茶 抖阴破解版 极乐禁地 91短视频 抖音Max TikTok成人版 PornHub 听泉鉴鲍 少女日记 草榴社区 哆哔涩漫 呦乐园 萝莉岛 悠悠禁区 拔萝卜 疯马秀

最近搜索

文件列表

  • 5. ARIMA/5. ARIMA in Code.mp4 127.5 MB
  • 16. Effective Learning Strategies for Machine Learning FAQ/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 113.4 MB
  • 5. ARIMA/15. Auto ARIMA in Code (Stocks).mp4 110.3 MB
  • 5. ARIMA/14. Auto ARIMA in Code.mp4 108.2 MB
  • 9. Deep Learning Convolutional Neural Networks (CNN)/7. CNN Architecture.mp4 101.5 MB
  • 12. VIP AWS Forecast/5. Code pt 2 (Uploading the data to S3).mp4 95.5 MB
  • 13. VIP Facebook Prophet/10. (The Dangers of) Prophet for Stock Price Prediction.mp4 95.4 MB
  • 8. Deep Learning Artificial Neural Networks (ANN)/5. Activation Functions.mp4 90.7 MB
  • 7. Machine Learning Methods/9. Machine Learning for Time Series Forecasting in Code (pt 1).mp4 90.4 MB
  • 10. Deep Learning Recurrent Neural Networks (RNN)/7. GRU and LSTM (pt 1).mp4 83.9 MB
  • 16. Effective Learning Strategies for Machine Learning FAQ/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 83.5 MB
  • 9. Deep Learning Convolutional Neural Networks (CNN)/2. What is Convolution.mp4 82.1 MB
  • 9. Deep Learning Convolutional Neural Networks (CNN)/5. Convolution on Color Images.mp4 77.6 MB
  • 8. Deep Learning Artificial Neural Networks (ANN)/8. Feedforward ANN for Time Series Forecasting Code.mp4 74.4 MB
  • 4. Exponential Smoothing and ETS Methods/8. SES Code.mp4 72.9 MB
  • 15. Extra Help With Python Coding for Beginners FAQ/3. Proof that using Jupyter Notebook is the same as not using it.mp4 72.9 MB
  • 7. Machine Learning Methods/2. Supervised Machine Learning Classification and Regression.mp4 72.3 MB
  • 3. Time Series Basics/11. Random Walks and the Random Walk Hypothesis.mp4 71.4 MB
  • 13. VIP Facebook Prophet/6. Prophet in Code Holidays and Exogenous Regressors.mp4 71.2 MB
  • 13. VIP Facebook Prophet/9. Prophet Multiplicative Seasonality, Outliers, Non-Daily Data.mp4 71.1 MB
  • 8. Deep Learning Artificial Neural Networks (ANN)/9. Feedforward ANN for Stock Return and Price Predictions Code.mp4 71.0 MB
  • 8. Deep Learning Artificial Neural Networks (ANN)/13. Human Activity Recognition Multi-Input ANN.mp4 70.8 MB
  • 5. ARIMA/17. Auto ARIMA in Code (Sales Data).mp4 68.6 MB
  • 7. Machine Learning Methods/8. Extrapolation and Stock Prices.mp4 67.9 MB
  • 13. VIP Facebook Prophet/3. Prophet Code Preparation.mp4 67.0 MB
  • 12. VIP AWS Forecast/4. Code pt 1 (Getting and Transforming the Data).mp4 66.4 MB
  • 10. Deep Learning Recurrent Neural Networks (RNN)/9. LSTMs for Time Series Forecasting in Code.mp4 65.4 MB
  • 6. Vector Autoregression (VAR, VMA, VARMA)/7. VARMA Econometrics Code (pt 2).mp4 64.6 MB
  • 5. ARIMA/7. Stationarity in Code.mp4 64.5 MB
  • 4. Exponential Smoothing and ETS Methods/14. Walk-Forward Validation in Code.mp4 63.2 MB
  • 6. Vector Autoregression (VAR, VMA, VARMA)/2. VAR and VARMA Theory.mp4 62.1 MB
  • 8. Deep Learning Artificial Neural Networks (ANN)/7. ANN Code Preparation.mp4 60.3 MB
  • 10. Deep Learning Recurrent Neural Networks (RNN)/6. RNNs Understanding by Implementing (Paying Attention to Shapes).mp4 58.2 MB
  • 13. VIP Facebook Prophet/5. Prophet in Code Fit, Forecast, Plot.mp4 57.9 MB
  • 5. ARIMA/6. Stationarity.mp4 57.8 MB
  • 13. VIP Facebook Prophet/4. Prophet in Code Data Preparation.mp4 57.4 MB
  • 12. VIP AWS Forecast/6. Code pt 3 (Building your Model).mp4 57.1 MB
  • 4. Exponential Smoothing and ETS Methods/4. SMA Code.mp4 56.7 MB
  • 8. Deep Learning Artificial Neural Networks (ANN)/4. The Geometrical Picture.mp4 56.6 MB
  • 5. ARIMA/2. Autoregressive Models - AR(p).mp4 55.1 MB
  • 6. Vector Autoregression (VAR, VMA, VARMA)/4. VARMA Code (pt 2).mp4 54.8 MB
  • 11. VIP GARCH/9. GARCH Code (pt 2).mp4 54.5 MB
  • 2. Getting Set Up/2. How to use Github & Extra Coding Tips (Optional).mp4 53.3 MB
  • 6. Vector Autoregression (VAR, VMA, VARMA)/6. VARMA Econometrics Code (pt 1).mp4 53.3 MB
  • 10. Deep Learning Recurrent Neural Networks (RNN)/8. GRU and LSTM (pt 2).mp4 52.7 MB
  • 8. Deep Learning Artificial Neural Networks (ANN)/16. How Does a Neural Network Learn.mp4 52.5 MB
  • 8. Deep Learning Artificial Neural Networks (ANN)/12. Human Activity Recognition Data Exploration.mp4 52.4 MB
  • 12. VIP AWS Forecast/7. Code pt 4 (Generating and Evaluating the Forecast).mp4 52.3 MB
  • 4. Exponential Smoothing and ETS Methods/12. Holt-Winters (Code).mp4 52.2 MB
  • 7. Machine Learning Methods/11. Machine Learning for Time Series Forecasting in Code (pt 2).mp4 51.8 MB
  • 6. Vector Autoregression (VAR, VMA, VARMA)/3. VARMA Code (pt 1).mp4 51.7 MB
  • 15. Extra Help With Python Coding for Beginners FAQ/2. How to Code by Yourself (part 2).mp4 51.6 MB
  • 12. VIP AWS Forecast/2. Data Model.mp4 51.3 MB
  • 9. Deep Learning Convolutional Neural Networks (CNN)/9. CNN for Time Series Forecasting in Code.mp4 51.1 MB
  • 4. Exponential Smoothing and ETS Methods/11. Holt-Winters (Theory).mp4 49.9 MB
  • 9. Deep Learning Convolutional Neural Networks (CNN)/10. CNN for Human Activity Recognition.mp4 48.6 MB
  • 11. VIP GARCH/13. A Deep Learning Approach to GARCH.mp4 48.3 MB
  • 5. ARIMA/13. Model Selection, AIC and BIC.mp4 48.1 MB
  • 6. Vector Autoregression (VAR, VMA, VARMA)/5. VARMA Code (pt 3).mp4 47.6 MB
  • 3. Time Series Basics/9. Financial Time Series Primer.mp4 47.1 MB
  • 8. Deep Learning Artificial Neural Networks (ANN)/3. Forward Propagation.mp4 47.0 MB
  • 4. Exponential Smoothing and ETS Methods/13. Walk-Forward Validation.mp4 46.5 MB
  • 10. Deep Learning Recurrent Neural Networks (RNN)/10. LSTMs for Time Series Classification in Code.mp4 46.2 MB
  • 11. VIP GARCH/10. GARCH Code (pt 3).mp4 46.1 MB
  • 8. Deep Learning Artificial Neural Networks (ANN)/2. The Neuron.mp4 46.0 MB
  • 3. Time Series Basics/8. Forecasting Metrics.mp4 45.8 MB
  • 8. Deep Learning Artificial Neural Networks (ANN)/6. Multiclass Classification.mp4 45.7 MB
  • 14. Setting Up Your Environment FAQ/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 45.7 MB
  • 12. VIP AWS Forecast/1. AWS Forecast Section Introduction.mp4 45.7 MB
  • 7. Machine Learning Methods/6. Machine Learning Algorithms Support Vector Machines.mp4 45.6 MB
  • 5. ARIMA/16. ACF and PACF for Stock Returns.mp4 45.6 MB
  • 7. Machine Learning Methods/12. Application Sales Data.mp4 44.2 MB
  • 13. VIP Facebook Prophet/7. Prophet in Code Cross-Validation.mp4 44.0 MB
  • 3. Time Series Basics/13. Naive Forecast and Forecasting Metrics in Code.mp4 43.5 MB
  • 5. ARIMA/4. ARIMA.mp4 43.4 MB
  • 5. ARIMA/10. ACF and PACF in Code (pt 1).mp4 43.3 MB
  • 11. VIP GARCH/11. GARCH Code (pt 4).mp4 43.3 MB
  • 13. VIP Facebook Prophet/2. How does Prophet work.mp4 42.7 MB
  • 2. Getting Set Up/1. Where to Get the Code.mp4 42.5 MB
  • 4. Exponential Smoothing and ETS Methods/16. Application Stock Predictions.mp4 42.5 MB
  • 4. Exponential Smoothing and ETS Methods/20. (Optional) More About State-Space Models.mp4 42.1 MB
  • 10. Deep Learning Recurrent Neural Networks (RNN)/3. Simple RNN Elman Unit (pt 2).mp4 42.0 MB
  • 11. VIP GARCH/7. GARCH Code Preparation (pt 2).mp4 42.0 MB
  • 5. ARIMA/12. Auto ARIMA and SARIMAX.mp4 41.4 MB
  • 4. Exponential Smoothing and ETS Methods/6. EWMA Code.mp4 41.3 MB
  • 16. Effective Learning Strategies for Machine Learning FAQ/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.8 MB
  • 10. Deep Learning Recurrent Neural Networks (RNN)/2. Simple RNN Elman Unit (pt 1).mp4 40.6 MB
  • 13. VIP Facebook Prophet/8. Prophet in Code Changepoint Detection.mp4 39.8 MB
  • 5. ARIMA/18. How to Forecast with ARIMA.mp4 39.8 MB
  • 11. VIP GARCH/6. GARCH Code Preparation (pt 1).mp4 39.8 MB
  • 17. Appendix FAQ Finale/2. BONUS Lecture.mp4 39.7 MB
  • 7. Machine Learning Methods/13. Application Predicting Stock Prices and Returns.mp4 39.2 MB
  • 6. Vector Autoregression (VAR, VMA, VARMA)/10. Converting Between Models (Optional).mp4 39.0 MB
  • 5. ARIMA/8. ACF (Autocorrelation Function).mp4 38.8 MB
  • 8. Deep Learning Artificial Neural Networks (ANN)/14. Human Activity Recognition Feature-Based Model.mp4 37.8 MB
  • 4. Exponential Smoothing and ETS Methods/5. EWMA Theory.mp4 37.6 MB
  • 4. Exponential Smoothing and ETS Methods/7. SES Theory.mp4 37.3 MB
  • 10. Deep Learning Recurrent Neural Networks (RNN)/5. RNN Code Preparation.mp4 35.8 MB
  • 5. ARIMA/11. ACF and PACF in Code (pt 2).mp4 35.5 MB
  • 3. Time Series Basics/7. Power, Log, and Box-Cox Transformations in Code.mp4 34.9 MB
  • 11. VIP GARCH/8. GARCH Code (pt 1).mp4 34.9 MB
  • 4. Exponential Smoothing and ETS Methods/9. Holt's Linear Trend Model (Theory).mp4 34.8 MB
  • 3. Time Series Basics/6. Power, Log, and Box-Cox Transformations.mp4 34.2 MB
  • 7. Machine Learning Methods/3. Autoregressive Machine Learning Models.mp4 34.0 MB
  • 3. Time Series Basics/2. What is a Time Series.mp4 33.8 MB
  • 7. Machine Learning Methods/7. Machine Learning Algorithms Random Forest.mp4 33.6 MB
  • 6. Vector Autoregression (VAR, VMA, VARMA)/9. Granger Causality Code.mp4 33.6 MB
  • 11. VIP GARCH/12. GARCH Code (pt 5).mp4 33.5 MB
  • 7. Machine Learning Methods/5. Machine Learning Algorithms Logistic Regression.mp4 33.3 MB
  • 8. Deep Learning Artificial Neural Networks (ANN)/11. Human Activity Recognition Code Preparation.mp4 32.8 MB
  • 11. VIP GARCH/14. GARCH Section Summary.mp4 32.3 MB
  • 8. Deep Learning Artificial Neural Networks (ANN)/10. Human Activity Recognition Dataset.mp4 32.2 MB
  • 1. Welcome/1. Introduction and Outline.mp4 32.2 MB
  • 9. Deep Learning Convolutional Neural Networks (CNN)/4. What is Convolution (Weight Sharing).mp4 31.9 MB
  • 3. Time Series Basics/12. The Naive Forecast and the Importance of Baselines.mp4 31.6 MB
  • 3. Time Series Basics/4. Why Do We Care About Shapes.mp4 30.9 MB
  • 4. Exponential Smoothing and ETS Methods/15. Application Sales Data.mp4 30.9 MB
  • 14. Setting Up Your Environment FAQ/1. Anaconda Environment Setup.mp4 29.2 MB
  • 9. Deep Learning Convolutional Neural Networks (CNN)/8. CNN Code Preparation.mp4 28.8 MB
  • 11. VIP GARCH/5. GARCH Theory.mp4 28.8 MB
  • 11. VIP GARCH/3. ARCH Theory (pt 2).mp4 28.5 MB
  • 3. Time Series Basics/15. Suggestion Box.mp4 28.5 MB
  • 7. Machine Learning Methods/14. Application Predicting Stock Movements.mp4 27.6 MB
  • 12. VIP AWS Forecast/9. AWS Forecast Section Summary.mp4 26.7 MB
  • 5. ARIMA/9. PACF (Partial Autocorrelation Funtion).mp4 26.3 MB
  • 15. Extra Help With Python Coding for Beginners FAQ/1. How to Code by Yourself (part 1).mp4 25.8 MB
  • 4. Exponential Smoothing and ETS Methods/2. Exponential Smoothing Intuition for Beginners.mp4 25.1 MB
  • 12. VIP AWS Forecast/3. Creating an IAM Role.mp4 25.0 MB
  • 9. Deep Learning Convolutional Neural Networks (CNN)/3. What is Convolution (Pattern-Matching).mp4 24.8 MB
  • 9. Deep Learning Convolutional Neural Networks (CNN)/6. Convolution for Time Series and ARIMA.mp4 24.8 MB
  • 3. Time Series Basics/5. Types of Tasks.mp4 24.7 MB
  • 1. Welcome/2. Warmup (Optional).mp4 24.3 MB
  • 5. ARIMA/1. ARIMA Section Introduction.mp4 24.1 MB
  • 6. Vector Autoregression (VAR, VMA, VARMA)/8. Granger Causality.mp4 23.5 MB
  • 7. Machine Learning Methods/4. Machine Learning Algorithms Linear Regression.mp4 22.9 MB
  • 8. Deep Learning Artificial Neural Networks (ANN)/15. Human Activity Recognition Combined Model.mp4 21.9 MB
  • 10. Deep Learning Recurrent Neural Networks (RNN)/1. RNN Section Introduction.mp4 21.5 MB
  • 11. VIP GARCH/4. ARCH Theory (pt 3).mp4 20.5 MB
  • 11. VIP GARCH/2. ARCH Theory (pt 1).mp4 20.5 MB
  • 8. Deep Learning Artificial Neural Networks (ANN)/1. Artificial Neural Networks Section Introduction.mp4 20.4 MB
  • 4. Exponential Smoothing and ETS Methods/17. SMA Application COVID-19 Counting.mp4 20.3 MB
  • 4. Exponential Smoothing and ETS Methods/19. Exponential Smoothing Section Summary.mp4 20.0 MB
  • 4. Exponential Smoothing and ETS Methods/10. Holt's Linear Trend Model (Code).mp4 20.0 MB
  • 7. Machine Learning Methods/10. Forecasting with Differencing.mp4 19.9 MB
  • 6. Vector Autoregression (VAR, VMA, VARMA)/11. Vector Autoregression Section Summary.mp4 19.6 MB
  • 10. Deep Learning Recurrent Neural Networks (RNN)/4. Aside State Space Models vs. RNNs.mp4 19.5 MB
  • 3. Time Series Basics/10. Price Simulations in Code.mp4 19.2 MB
  • 11. VIP GARCH/1. GARCH Section Introduction.mp4 19.1 MB
  • 7. Machine Learning Methods/1. Machine Learning Section Introduction.mp4 18.4 MB
  • 3. Time Series Basics/1. Time Series Basics Section Introduction.mp4 18.3 MB
  • 17. Appendix FAQ Finale/1. What is the Appendix.mp4 17.2 MB
  • 10. Deep Learning Recurrent Neural Networks (RNN)/12. RNN Section Summary.mp4 16.7 MB
  • 10. Deep Learning Recurrent Neural Networks (RNN)/11. The Unreasonable Ineffectiveness of Recurrent Neural Networks.mp4 16.2 MB
  • 9. Deep Learning Convolutional Neural Networks (CNN)/11. CNN Section Summary.mp4 16.2 MB
  • 4. Exponential Smoothing and ETS Methods/3. SMA Theory.mp4 16.0 MB
  • 13. VIP Facebook Prophet/1. Prophet Section Introduction.mp4 15.2 MB
  • 9. Deep Learning Convolutional Neural Networks (CNN)/1. CNN Section Introduction.mp4 15.0 MB
  • 12. VIP AWS Forecast/8. AWS Forecast Exercise.mp4 14.4 MB
  • 4. Exponential Smoothing and ETS Methods/1. Exponential Smoothing Section Introduction.mp4 14.2 MB
  • 3. Time Series Basics/3. Modeling vs. Predicting.mp4 14.1 MB
  • 13. VIP Facebook Prophet/11. Prophet Section Summary.mp4 14.1 MB
  • 5. ARIMA/20. ARIMA Section Summary.mp4 13.4 MB
  • 16. Effective Learning Strategies for Machine Learning FAQ/1. How to Succeed in this Course (Long Version).mp4 13.2 MB
  • 6. Vector Autoregression (VAR, VMA, VARMA)/1. Vector Autoregression Section Introduction.mp4 13.0 MB
  • 3. Time Series Basics/14. Time Series Basics Section Summary.mp4 12.7 MB
  • 4. Exponential Smoothing and ETS Methods/18. SMA Application Algorithmic Trading.mp4 12.2 MB
  • 8. Deep Learning Artificial Neural Networks (ANN)/17. Artificial Neural Networks Section Summary.mp4 11.5 MB
  • 5. ARIMA/3. Moving Average Models - MA(q).mp4 11.4 MB
  • 7. Machine Learning Methods/15. Machine Learning Section Summary.mp4 10.9 MB
  • 5. ARIMA/19. Forecasting Out-Of-Sample.mp4 7.1 MB
  • 9. Deep Learning Convolutional Neural Networks (CNN)/7. CNN Architecture.srt 32.8 kB
  • 16. Effective Learning Strategies for Machine Learning FAQ/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 32.6 kB
  • 16. Effective Learning Strategies for Machine Learning FAQ/4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt 24.1 kB
  • 5. ARIMA/5. ARIMA in Code.srt 23.4 kB
  • 8. Deep Learning Artificial Neural Networks (ANN)/5. Activation Functions.srt 23.4 kB
  • 10. Deep Learning Recurrent Neural Networks (RNN)/7. GRU and LSTM (pt 1).srt 23.4 kB
  • 15. Extra Help With Python Coding for Beginners FAQ/1. How to Code by Yourself (part 1).srt 23.2 kB
  • 9. Deep Learning Convolutional Neural Networks (CNN)/5. Convolution on Color Images.srt 21.3 kB
  • 9. Deep Learning Convolutional Neural Networks (CNN)/2. What is Convolution.srt 21.2 kB
  • 14. Setting Up Your Environment FAQ/1. Anaconda Environment Setup.srt 20.8 kB
  • 3. Time Series Basics/11. Random Walks and the Random Walk Hypothesis.srt 19.8 kB
  • 7. Machine Learning Methods/2. Supervised Machine Learning Classification and Regression.srt 19.4 kB
  • 6. Vector Autoregression (VAR, VMA, VARMA)/2. VAR and VARMA Theory.srt 18.2 kB
  • 5. ARIMA/6. Stationarity.srt 18.0 kB
  • 5. ARIMA/15. Auto ARIMA in Code (Stocks).srt 17.5 kB
  • 16. Effective Learning Strategies for Machine Learning FAQ/3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt 17.2 kB
  • 5. ARIMA/2. Autoregressive Models - AR(p).srt 17.1 kB
  • 12. VIP AWS Forecast/5. Code pt 2 (Uploading the data to S3).srt 16.8 kB
  • 8. Deep Learning Artificial Neural Networks (ANN)/7. ANN Code Preparation.srt 16.7 kB
  • 13. VIP Facebook Prophet/3. Prophet Code Preparation.srt 16.6 kB
  • 5. ARIMA/14. Auto ARIMA in Code.srt 16.1 kB
  • 3. Time Series Basics/8. Forecasting Metrics.srt 15.6 kB
  • 11. VIP GARCH/13. A Deep Learning Approach to GARCH.srt 15.4 kB
  • 3. Time Series Basics/9. Financial Time Series Primer.srt 15.4 kB
  • 4. Exponential Smoothing and ETS Methods/11. Holt-Winters (Theory).srt 15.4 kB
  • 7. Machine Learning Methods/9. Machine Learning for Time Series Forecasting in Code (pt 1).srt 15.3 kB
  • 10. Deep Learning Recurrent Neural Networks (RNN)/8. GRU and LSTM (pt 2).srt 15.2 kB
  • 6. Vector Autoregression (VAR, VMA, VARMA)/10. Converting Between Models (Optional).srt 15.1 kB
  • 16. Effective Learning Strategies for Machine Learning FAQ/1. How to Succeed in this Course (Long Version).srt 15.0 kB
  • 4. Exponential Smoothing and ETS Methods/5. EWMA Theory.srt 14.9 kB
  • 4. Exponential Smoothing and ETS Methods/8. SES Code.srt 14.9 kB
  • 4. Exponential Smoothing and ETS Methods/20. (Optional) More About State-Space Models.srt 14.6 kB
  • 14. Setting Up Your Environment FAQ/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 14.6 kB
  • 8. Deep Learning Artificial Neural Networks (ANN)/16. How Does a Neural Network Learn.srt 14.5 kB
  • 15. Extra Help With Python Coding for Beginners FAQ/3. Proof that using Jupyter Notebook is the same as not using it.srt 14.4 kB
  • 13. VIP Facebook Prophet/10. (The Dangers of) Prophet for Stock Price Prediction.srt 14.3 kB
  • 4. Exponential Smoothing and ETS Methods/7. SES Theory.srt 14.2 kB
  • 5. ARIMA/4. ARIMA.srt 14.1 kB
  • 5. ARIMA/13. Model Selection, AIC and BIC.srt 13.8 kB
  • 8. Deep Learning Artificial Neural Networks (ANN)/13. Human Activity Recognition Multi-Input ANN.srt 13.8 kB
  • 15. Extra Help With Python Coding for Beginners FAQ/2. How to Code by Yourself (part 2).srt 13.5 kB
  • 7. Machine Learning Methods/6. Machine Learning Algorithms Support Vector Machines.srt 13.5 kB
  • 5. ARIMA/8. ACF (Autocorrelation Function).srt 13.3 kB
  • 2. Getting Set Up/2. How to use Github & Extra Coding Tips (Optional).srt 13.3 kB
  • 10. Deep Learning Recurrent Neural Networks (RNN)/3. Simple RNN Elman Unit (pt 2).srt 13.2 kB
  • 12. VIP AWS Forecast/4. Code pt 1 (Getting and Transforming the Data).srt 13.2 kB
  • 8. Deep Learning Artificial Neural Networks (ANN)/2. The Neuron.srt 13.0 kB
  • 8. Deep Learning Artificial Neural Networks (ANN)/3. Forward Propagation.srt 12.8 kB
  • 4. Exponential Smoothing and ETS Methods/13. Walk-Forward Validation.srt 12.6 kB
  • 5. ARIMA/12. Auto ARIMA and SARIMAX.srt 12.6 kB
  • 12. VIP AWS Forecast/2. Data Model.srt 12.5 kB
  • 5. ARIMA/18. How to Forecast with ARIMA.srt 12.4 kB
  • 8. Deep Learning Artificial Neural Networks (ANN)/4. The Geometrical Picture.srt 12.0 kB
  • 10. Deep Learning Recurrent Neural Networks (RNN)/2. Simple RNN Elman Unit (pt 1).srt 11.8 kB
  • 13. VIP Facebook Prophet/6. Prophet in Code Holidays and Exogenous Regressors.srt 11.6 kB
  • 10. Deep Learning Recurrent Neural Networks (RNN)/5. RNN Code Preparation.srt 11.4 kB
  • 8. Deep Learning Artificial Neural Networks (ANN)/6. Multiclass Classification.srt 11.4 kB
  • 13. VIP Facebook Prophet/2. How does Prophet work.srt 11.1 kB
  • 5. ARIMA/7. Stationarity in Code.srt 11.0 kB
  • 8. Deep Learning Artificial Neural Networks (ANN)/8. Feedforward ANN for Time Series Forecasting Code.srt 11.0 kB
  • 12. VIP AWS Forecast/1. AWS Forecast Section Introduction.srt 10.9 kB
  • 2. Getting Set Up/1. Where to Get the Code.srt 10.8 kB
  • 11. VIP GARCH/6. GARCH Code Preparation (pt 1).srt 10.7 kB
  • 6. Vector Autoregression (VAR, VMA, VARMA)/7. VARMA Econometrics Code (pt 2).srt 10.7 kB
  • 11. VIP GARCH/7. GARCH Code Preparation (pt 2).srt 10.6 kB
  • 5. ARIMA/17. Auto ARIMA in Code (Sales Data).srt 10.4 kB
  • 7. Machine Learning Methods/3. Autoregressive Machine Learning Models.srt 10.4 kB
  • 4. Exponential Smoothing and ETS Methods/9. Holt's Linear Trend Model (Theory).srt 10.3 kB
  • 4. Exponential Smoothing and ETS Methods/14. Walk-Forward Validation in Code.srt 10.3 kB
  • 10. Deep Learning Recurrent Neural Networks (RNN)/6. RNNs Understanding by Implementing (Paying Attention to Shapes).srt 10.2 kB
  • 7. Machine Learning Methods/8. Extrapolation and Stock Prices.srt 10.0 kB
  • 6. Vector Autoregression (VAR, VMA, VARMA)/6. VARMA Econometrics Code (pt 1).srt 9.9 kB
  • 4. Exponential Smoothing and ETS Methods/4. SMA Code.srt 9.9 kB
  • 13. VIP Facebook Prophet/4. Prophet in Code Data Preparation.srt 9.9 kB
  • 13. VIP Facebook Prophet/9. Prophet Multiplicative Seasonality, Outliers, Non-Daily Data.srt 9.9 kB
  • 11. VIP GARCH/5. GARCH Theory.srt 9.8 kB
  • 11. VIP GARCH/3. ARCH Theory (pt 2).srt 9.8 kB
  • 4. Exponential Smoothing and ETS Methods/6. EWMA Code.srt 9.8 kB
  • 4. Exponential Smoothing and ETS Methods/12. Holt-Winters (Code).srt 9.8 kB
  • 5. ARIMA/10. ACF and PACF in Code (pt 1).srt 9.6 kB
  • 13. VIP Facebook Prophet/5. Prophet in Code Fit, Forecast, Plot.srt 9.5 kB
  • 12. VIP AWS Forecast/6. Code pt 3 (Building your Model).srt 9.5 kB
  • 3. Time Series Basics/12. The Naive Forecast and the Importance of Baselines.srt 9.4 kB
  • 10. Deep Learning Recurrent Neural Networks (RNN)/9. LSTMs for Time Series Forecasting in Code.srt 9.4 kB
  • 7. Machine Learning Methods/7. Machine Learning Algorithms Random Forest.srt 9.3 kB
  • 8. Deep Learning Artificial Neural Networks (ANN)/9. Feedforward ANN for Stock Return and Price Predictions Code.srt 9.2 kB
  • 7. Machine Learning Methods/5. Machine Learning Algorithms Logistic Regression.srt 9.2 kB
  • 3. Time Series Basics/5. Types of Tasks.srt 9.1 kB
  • 11. VIP GARCH/14. GARCH Section Summary.srt 8.9 kB
  • 11. VIP GARCH/9. GARCH Code (pt 2).srt 8.9 kB
  • 12. VIP AWS Forecast/7. Code pt 4 (Generating and Evaluating the Forecast).srt 8.9 kB
  • 8. Deep Learning Artificial Neural Networks (ANN)/12. Human Activity Recognition Data Exploration.srt 8.8 kB
  • 9. Deep Learning Convolutional Neural Networks (CNN)/4. What is Convolution (Weight Sharing).srt 8.8 kB
  • 6. Vector Autoregression (VAR, VMA, VARMA)/3. VARMA Code (pt 1).srt 8.7 kB
  • 3. Time Series Basics/13. Naive Forecast and Forecasting Metrics in Code.srt 8.5 kB
  • 3. Time Series Basics/6. Power, Log, and Box-Cox Transformations.srt 8.3 kB
  • 5. ARIMA/11. ACF and PACF in Code (pt 2).srt 8.2 kB
  • 5. ARIMA/9. PACF (Partial Autocorrelation Funtion).srt 8.2 kB
  • 9. Deep Learning Convolutional Neural Networks (CNN)/8. CNN Code Preparation.srt 8.1 kB
  • 8. Deep Learning Artificial Neural Networks (ANN)/11. Human Activity Recognition Code Preparation.srt 8.1 kB
  • 17. Appendix FAQ Finale/2. BONUS Lecture.srt 8.1 kB
  • 3. Time Series Basics/4. Why Do We Care About Shapes.srt 7.8 kB
  • 1. Welcome/1. Introduction and Outline.srt 7.7 kB
  • 5. ARIMA/16. ACF and PACF for Stock Returns.srt 7.7 kB
  • 6. Vector Autoregression (VAR, VMA, VARMA)/5. VARMA Code (pt 3).srt 7.6 kB
  • 8. Deep Learning Artificial Neural Networks (ANN)/10. Human Activity Recognition Dataset.srt 7.4 kB
  • 4. Exponential Smoothing and ETS Methods/2. Exponential Smoothing Intuition for Beginners.srt 7.4 kB
  • 5. ARIMA/1. ARIMA Section Introduction.srt 7.3 kB
  • 11. VIP GARCH/10. GARCH Code (pt 3).srt 7.3 kB
  • 6. Vector Autoregression (VAR, VMA, VARMA)/4. VARMA Code (pt 2).srt 7.2 kB
  • 9. Deep Learning Convolutional Neural Networks (CNN)/3. What is Convolution (Pattern-Matching).srt 7.1 kB
  • 3. Time Series Basics/7. Power, Log, and Box-Cox Transformations in Code.srt 7.0 kB
  • 12. VIP AWS Forecast/9. AWS Forecast Section Summary.srt 7.0 kB
  • 9. Deep Learning Convolutional Neural Networks (CNN)/9. CNN for Time Series Forecasting in Code.srt 6.9 kB
  • 7. Machine Learning Methods/11. Machine Learning for Time Series Forecasting in Code (pt 2).srt 6.8 kB
  • 11. VIP GARCH/4. ARCH Theory (pt 3).srt 6.8 kB
  • 7. Machine Learning Methods/4. Machine Learning Algorithms Linear Regression.srt 6.6 kB
  • 9. Deep Learning Convolutional Neural Networks (CNN)/10. CNN for Human Activity Recognition.srt 6.6 kB
  • 9. Deep Learning Convolutional Neural Networks (CNN)/6. Convolution for Time Series and ARIMA.srt 6.6 kB
  • 10. Deep Learning Recurrent Neural Networks (RNN)/1. RNN Section Introduction.srt 6.5 kB
  • 3. Time Series Basics/2. What is a Time Series.srt 6.5 kB
  • 11. VIP GARCH/2. ARCH Theory (pt 1).srt 6.5 kB
  • 4. Exponential Smoothing and ETS Methods/16. Application Stock Predictions.srt 6.5 kB
  • 11. VIP GARCH/8. GARCH Code (pt 1).srt 6.3 kB
  • 13. VIP Facebook Prophet/7. Prophet in Code Cross-Validation.srt 6.2 kB
  • 1. Welcome/2. Warmup (Optional).srt 6.2 kB
  • 3. Time Series Basics/1. Time Series Basics Section Introduction.srt 6.0 kB
  • 11. VIP GARCH/11. GARCH Code (pt 4).srt 6.0 kB
  • 8. Deep Learning Artificial Neural Networks (ANN)/14. Human Activity Recognition Feature-Based Model.srt 5.6 kB
  • 10. Deep Learning Recurrent Neural Networks (RNN)/10. LSTMs for Time Series Classification in Code.srt 5.6 kB
  • 4. Exponential Smoothing and ETS Methods/19. Exponential Smoothing Section Summary.srt 5.5 kB
  • 7. Machine Learning Methods/1. Machine Learning Section Introduction.srt 5.5 kB
  • 7. Machine Learning Methods/12. Application Sales Data.srt 5.5 kB
  • 6. Vector Autoregression (VAR, VMA, VARMA)/8. Granger Causality.srt 5.4 kB
  • 7. Machine Learning Methods/10. Forecasting with Differencing.srt 5.4 kB
  • 4. Exponential Smoothing and ETS Methods/15. Application Sales Data.srt 5.3 kB
  • 11. VIP GARCH/1. GARCH Section Introduction.srt 5.3 kB
  • 4. Exponential Smoothing and ETS Methods/3. SMA Theory.srt 5.0 kB
  • 7. Machine Learning Methods/13. Application Predicting Stock Prices and Returns.srt 4.9 kB
  • 12. VIP AWS Forecast/3. Creating an IAM Role.srt 4.9 kB
  • 3. Time Series Basics/15. Suggestion Box.srt 4.9 kB
  • 6. Vector Autoregression (VAR, VMA, VARMA)/11. Vector Autoregression Section Summary.srt 4.8 kB
  • 5. ARIMA/20. ARIMA Section Summary.srt 4.7 kB
  • 13. VIP Facebook Prophet/11. Prophet Section Summary.srt 4.6 kB
  • 7. Machine Learning Methods/14. Application Predicting Stock Movements.srt 4.6 kB
  • 8. Deep Learning Artificial Neural Networks (ANN)/1. Artificial Neural Networks Section Introduction.srt 4.5 kB
  • 10. Deep Learning Recurrent Neural Networks (RNN)/4. Aside State Space Models vs. RNNs.srt 4.5 kB
  • 3. Time Series Basics/14. Time Series Basics Section Summary.srt 4.4 kB
  • 5. ARIMA/3. Moving Average Models - MA(q).srt 4.3 kB
  • 13. VIP Facebook Prophet/8. Prophet in Code Changepoint Detection.srt 4.3 kB
  • 4. Exponential Smoothing and ETS Methods/17. SMA Application COVID-19 Counting.srt 4.3 kB
  • 10. Deep Learning Recurrent Neural Networks (RNN)/11. The Unreasonable Ineffectiveness of Recurrent Neural Networks.srt 4.3 kB
  • 9. Deep Learning Convolutional Neural Networks (CNN)/11. CNN Section Summary.srt 4.3 kB
  • 13. VIP Facebook Prophet/1. Prophet Section Introduction.srt 4.2 kB
  • 11. VIP GARCH/12. GARCH Code (pt 5).srt 4.2 kB
  • 9. Deep Learning Convolutional Neural Networks (CNN)/1. CNN Section Introduction.srt 4.1 kB
  • 4. Exponential Smoothing and ETS Methods/1. Exponential Smoothing Section Introduction.srt 4.0 kB
  • 10. Deep Learning Recurrent Neural Networks (RNN)/12. RNN Section Summary.srt 3.9 kB
  • 17. Appendix FAQ Finale/1. What is the Appendix.srt 3.9 kB
  • 12. VIP AWS Forecast/8. AWS Forecast Exercise.srt 3.7 kB
  • 6. Vector Autoregression (VAR, VMA, VARMA)/9. Granger Causality Code.srt 3.6 kB
  • 4. Exponential Smoothing and ETS Methods/10. Holt's Linear Trend Model (Code).srt 3.5 kB
  • 3. Time Series Basics/10. Price Simulations in Code.srt 3.5 kB
  • 3. Time Series Basics/3. Modeling vs. Predicting.srt 3.4 kB
  • 6. Vector Autoregression (VAR, VMA, VARMA)/1. Vector Autoregression Section Introduction.srt 3.2 kB
  • 8. Deep Learning Artificial Neural Networks (ANN)/15. Human Activity Recognition Combined Model.srt 3.1 kB
  • 7. Machine Learning Methods/15. Machine Learning Section Summary.srt 3.1 kB
  • 4. Exponential Smoothing and ETS Methods/18. SMA Application Algorithmic Trading.srt 2.9 kB
  • 8. Deep Learning Artificial Neural Networks (ANN)/17. Artificial Neural Networks Section Summary.srt 2.9 kB
  • 5. ARIMA/19. Forecasting Out-Of-Sample.srt 1.7 kB
  • 13. VIP Facebook Prophet/How you can help GetFreeCourses.Co.txt 182 Bytes
  • 4. Exponential Smoothing and ETS Methods/How you can help GetFreeCourses.Co.txt 182 Bytes
  • 9. Deep Learning Convolutional Neural Networks (CNN)/How you can help GetFreeCourses.Co.txt 182 Bytes
  • How you can help GetFreeCourses.Co.txt 182 Bytes
  • 2. Getting Set Up/1.1 Data Links.html 157 Bytes
  • 2. Getting Set Up/1.2 Github Link.html 143 Bytes
  • 13. VIP Facebook Prophet/GetFreeCourses.Co.url 116 Bytes
  • 4. Exponential Smoothing and ETS Methods/GetFreeCourses.Co.url 116 Bytes
  • 9. Deep Learning Convolutional Neural Networks (CNN)/GetFreeCourses.Co.url 116 Bytes
  • Download Paid Udemy Courses For Free.url 116 Bytes
  • GetFreeCourses.Co.url 116 Bytes
  • 9. Deep Learning Convolutional Neural Networks (CNN)/11.1 Convert a Time Series Into an Image with Gramian Angular Fields and Markov Transition Fields.html 0 Bytes

随机展示

相关说明

本站不存储任何资源内容，只收集BT种子元数据(例如文件名和文件大小)和磁力链接(BT种子标识符)，并提供查询服务，是一个完全合法的搜索引擎系统。 网站不提供种子下载服务，用户可以通过第三方链接或磁力链接获取到相关的种子资源。本站也不对BT种子真实性及合法性负责，请用户注意甄别！