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

Creation Time	Sept. 14, 2023, 2:39 a.m.
Last Access Time	Nov. 14, 2024, 8:09 p.m.
File Size	4.8 GB
Keywords	and Co-Udemy-Time Series Forecasting Analysis Machine GetFreeCourses Learning
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Total Files	244
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04 ARIMA/005 ARIMA in Code.mp4 121.6 MB
12 Effective Learning Strategies for Machine Learning FAQ/004 Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 108.2 MB
04 ARIMA/015 Auto ARIMA in Code (Stocks).mp4 105.2 MB
04 ARIMA/014 Auto ARIMA in Code.mp4 103.2 MB
07 Deep Learning_ Convolutional Neural Networks (CNN)/007 CNN Architecture.mp4 96.8 MB
08 VIP_ AWS Forecast/005 Code pt 2 (Uploading the data to S3).mp4 91.1 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/005 Activation Functions.mp4 86.5 MB
05 Machine Learning Methods/009 Machine Learning for Time Series Forecasting in Code (pt 1).mp4 86.2 MB
12 Effective Learning Strategies for Machine Learning FAQ/003 Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 79.6 MB
07 Deep Learning_ Convolutional Neural Networks (CNN)/002 What is Convolution_.mp4 78.3 MB
07 Deep Learning_ Convolutional Neural Networks (CNN)/005 Convolution on Color Images.mp4 74.0 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/008 Feedforward ANN for Time Series Forecasting Code.mp4 70.9 MB
03 Exponential Smoothing and ETS Methods/008 SES Code.mp4 69.5 MB
11 Extra Help With Python Coding for Beginners FAQ/003 Proof that using Jupyter Notebook is the same as not using it.mp4 69.5 MB
05 Machine Learning Methods/002 Supervised Machine Learning_ Classification and Regression.mp4 69.0 MB
02 Time Series Basics/011 Random Walks and the Random Walk Hypothesis.mp4 68.1 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/009 Feedforward ANN for Stock Return and Price Predictions Code.mp4 67.7 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/013 Human Activity Recognition_ Multi-Input ANN.mp4 67.5 MB
04 ARIMA/017 Auto ARIMA in Code (Sales Data).mp4 65.4 MB
05 Machine Learning Methods/008 Extrapolation and Stock Prices.mp4 64.7 MB
08 VIP_ AWS Forecast/004 Code pt 1 (Getting and Transforming the Data).mp4 63.3 MB
01 Welcome/002 Where to Get the Code.mp4 62.0 MB
04 ARIMA/007 Stationarity in Code.mp4 61.5 MB
03 Exponential Smoothing and ETS Methods/014 Walk-Forward Validation in Code.mp4 60.2 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/007 ANN Code Preparation.mp4 57.5 MB
04 ARIMA/006 Stationarity.mp4 55.1 MB
08 VIP_ AWS Forecast/006 Code pt 3 (Building your Model).mp4 54.5 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/004 The Geometrical Picture.mp4 54.0 MB
03 Exponential Smoothing and ETS Methods/004 SMA Code.mp4 53.6 MB
04 ARIMA/002 Autoregressive Models - AR(p).mp4 52.5 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/016 How Does a Neural Network _Learn__.mp4 50.1 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/012 Human Activity Recognition_ Data Exploration.mp4 50.0 MB
08 VIP_ AWS Forecast/007 Code pt 4 (Generating and Evaluating the Forecast).mp4 49.9 MB
03 Exponential Smoothing and ETS Methods/012 Holt-Winters (Code).mp4 49.8 MB
05 Machine Learning Methods/011 Machine Learning for Time Series Forecasting in Code (pt 2).mp4 49.4 MB
11 Extra Help With Python Coding for Beginners FAQ/002 How to Code by Yourself (part 2).mp4 49.2 MB
08 VIP_ AWS Forecast/002 Data Model.mp4 49.0 MB
07 Deep Learning_ Convolutional Neural Networks (CNN)/009 CNN for Time Series Forecasting in Code.mp4 48.8 MB
03 Exponential Smoothing and ETS Methods/011 Holt-Winters (Theory).mp4 47.6 MB
07 Deep Learning_ Convolutional Neural Networks (CNN)/010 CNN for Human Activity Recognition.mp4 46.4 MB
04 ARIMA/013 Model Selection, AIC and BIC.mp4 45.9 MB
02 Time Series Basics/009 Financial Time Series Primer.mp4 44.9 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/003 Forward Propagation.mp4 44.8 MB
03 Exponential Smoothing and ETS Methods/013 Walk-Forward Validation.mp4 44.3 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/002 The Neuron.mp4 43.9 MB
02 Time Series Basics/008 Forecasting Metrics.mp4 43.7 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/006 Multiclass Classification.mp4 43.6 MB
10 Setting Up Your Environment FAQ/002 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 43.6 MB
08 VIP_ AWS Forecast/001 AWS Forecast Section Introduction.mp4 43.5 MB
05 Machine Learning Methods/006 Machine Learning Algorithms_ Support Vector Machines.mp4 43.5 MB
04 ARIMA/016 ACF and PACF for Stock Returns.mp4 43.5 MB
05 Machine Learning Methods/012 Application_ Sales Data.mp4 42.2 MB
02 Time Series Basics/013 Naive Forecast and Forecasting Metrics in Code.mp4 41.5 MB
04 ARIMA/004 ARIMA.mp4 41.4 MB
04 ARIMA/010 ACF and PACF in Code (pt 1).mp4 41.3 MB
03 Exponential Smoothing and ETS Methods/016 Application_ Stock Predictions.mp4 40.5 MB
04 ARIMA/012 Auto ARIMA and SARIMAX.mp4 39.4 MB
03 Exponential Smoothing and ETS Methods/006 EWMA Code.mp4 39.4 MB
12 Effective Learning Strategies for Machine Learning FAQ/002 Is this for Beginners or Experts_ Academic or Practical_ Fast or slow-paced_.mp4 39.0 MB
04 ARIMA/018 How to Forecast with ARIMA.mp4 37.9 MB
13 Appendix _ FAQ Finale/002 BONUS_ Where to get discount coupons and FREE deep learning material.mp4 37.8 MB
05 Machine Learning Methods/013 Application_ Predicting Stock Prices and Returns.mp4 37.4 MB
04 ARIMA/008 ACF (Autocorrelation Function).mp4 37.0 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/014 Human Activity Recognition_ Feature-Based Model.mp4 36.1 MB
03 Exponential Smoothing and ETS Methods/005 EWMA Theory.mp4 35.8 MB
03 Exponential Smoothing and ETS Methods/007 SES Theory.mp4 35.6 MB
04 ARIMA/011 ACF and PACF in Code (pt 2).mp4 33.9 MB
02 Time Series Basics/007 Power, Log, and Box-Cox Transformations in Code.mp4 33.3 MB
03 Exponential Smoothing and ETS Methods/009 Holt's Linear Trend Model (Theory).mp4 33.2 MB
02 Time Series Basics/006 Power, Log, and Box-Cox Transformations.mp4 32.6 MB
05 Machine Learning Methods/003 Autoregressive Machine Learning Models.mp4 32.4 MB
02 Time Series Basics/002 What is a Time Series_.mp4 32.2 MB
05 Machine Learning Methods/007 Machine Learning Algorithms_ Random Forest.mp4 32.0 MB
05 Machine Learning Methods/005 Machine Learning Algorithms_ Logistic Regression.mp4 31.7 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/011 Human Activity Recognition_ Code Preparation.mp4 31.3 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/010 Human Activity Recognition Dataset.mp4 30.7 MB
01 Welcome/001 Introduction and Outline.mp4 30.7 MB
07 Deep Learning_ Convolutional Neural Networks (CNN)/004 What is Convolution_ (Weight Sharing).mp4 30.4 MB
02 Time Series Basics/012 The Naive Forecast and the Importance of Baselines.mp4 30.1 MB
02 Time Series Basics/004 Why Do We Care About Shapes_.mp4 29.5 MB
03 Exponential Smoothing and ETS Methods/015 Application_ Sales Data.mp4 29.4 MB
10 Setting Up Your Environment FAQ/001 Anaconda Environment Setup.mp4 27.9 MB
07 Deep Learning_ Convolutional Neural Networks (CNN)/008 CNN Code Preparation.mp4 27.5 MB
05 Machine Learning Methods/014 Application_ Predicting Stock Movements.mp4 26.3 MB
08 VIP_ AWS Forecast/009 AWS Forecast Section Summary.mp4 25.5 MB
04 ARIMA/009 PACF (Partial Autocorrelation Funtion).mp4 25.1 MB
11 Extra Help With Python Coding for Beginners FAQ/001 How to Code by Yourself (part 1).mp4 24.6 MB
03 Exponential Smoothing and ETS Methods/002 Exponential Smoothing Intuition for Beginners.mp4 23.9 MB
08 VIP_ AWS Forecast/003 Creating an IAM Role.mp4 23.8 MB
07 Deep Learning_ Convolutional Neural Networks (CNN)/003 What is Convolution_ (Pattern-Matching).mp4 23.7 MB
07 Deep Learning_ Convolutional Neural Networks (CNN)/006 Convolution for Time Series and ARIMA.mp4 23.6 MB
02 Time Series Basics/005 Types of Tasks.mp4 23.5 MB
01 Welcome/003 Warmup (Optional).mp4 23.2 MB
04 ARIMA/001 ARIMA Section Introduction.mp4 23.0 MB
05 Machine Learning Methods/004 Machine Learning Algorithms_ Linear Regression.mp4 21.8 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/015 Human Activity Recognition_ Combined Model.mp4 20.9 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/001 Artificial Neural Networks_ Section Introduction.mp4 19.4 MB
03 Exponential Smoothing and ETS Methods/017 SMA Application_ COVID-19 Counting.mp4 19.4 MB
03 Exponential Smoothing and ETS Methods/019 Exponential Smoothing Section Summary.mp4 19.1 MB
03 Exponential Smoothing and ETS Methods/010 Holt's Linear Trend Model (Code).mp4 19.0 MB
05 Machine Learning Methods/010 Forecasting with Differencing.mp4 19.0 MB
02 Time Series Basics/010 Price Simulations in Code.mp4 18.3 MB
05 Machine Learning Methods/001 Machine Learning Section Introduction.mp4 17.5 MB
02 Time Series Basics/001 Time Series Basics Section Introduction.mp4 17.5 MB
13 Appendix _ FAQ Finale/001 What is the Appendix_.mp4 16.4 MB
02 Time Series Basics/015 Suggestion Box.mp4 16.1 MB
07 Deep Learning_ Convolutional Neural Networks (CNN)/011 CNN Section Summary.mp4 15.4 MB
03 Exponential Smoothing and ETS Methods/003 SMA Theory.mp4 15.2 MB
07 Deep Learning_ Convolutional Neural Networks (CNN)/001 CNN Section Introduction.mp4 14.3 MB
08 VIP_ AWS Forecast/008 AWS Forecast Exercise.mp4 13.8 MB
03 Exponential Smoothing and ETS Methods/001 Exponential Smoothing Section Introduction.mp4 13.6 MB
02 Time Series Basics/003 Modeling vs. Predicting.mp4 13.5 MB
04 ARIMA/019 ARIMA Section Summary.mp4 12.7 MB
12 Effective Learning Strategies for Machine Learning FAQ/001 How to Succeed in this Course (Long Version).mp4 12.6 MB
02 Time Series Basics/014 Time Series Basics Section Summary.mp4 12.1 MB
03 Exponential Smoothing and ETS Methods/018 SMA Application_ Algorithmic Trading.mp4 11.6 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/017 Artificial Neural Networks_ Section Summary.mp4 10.9 MB
05 Machine Learning Methods/015 Machine Learning Section Summary.mp4 10.4 MB
04 ARIMA/003 Moving Average Models - MA(q).mp4 10.1 MB
07 Deep Learning_ Convolutional Neural Networks (CNN)/007 CNN Architecture.en.srt 33.2 KB
12 Effective Learning Strategies for Machine Learning FAQ/002 Is this for Beginners or Experts_ Academic or Practical_ Fast or slow-paced_.en.srt 33.0 KB
12 Effective Learning Strategies for Machine Learning FAQ/004 Machine Learning and AI Prerequisite Roadmap (pt 2).en.srt 24.4 KB
04 ARIMA/005 ARIMA in Code.en.srt 23.7 KB
06 Deep Learning_ Artificial Neural Networks (ANN)/005 Activation Functions.en.srt 23.7 KB
11 Extra Help With Python Coding for Beginners FAQ/001 How to Code by Yourself (part 1).en.srt 23.5 KB
07 Deep Learning_ Convolutional Neural Networks (CNN)/005 Convolution on Color Images.en.srt 21.6 KB
07 Deep Learning_ Convolutional Neural Networks (CNN)/002 What is Convolution_.en.srt 21.4 KB
10 Setting Up Your Environment FAQ/001 Anaconda Environment Setup.en.srt 21.1 KB
02 Time Series Basics/011 Random Walks and the Random Walk Hypothesis.en.srt 20.1 KB
05 Machine Learning Methods/002 Supervised Machine Learning_ Classification and Regression.en.srt 19.6 KB
04 ARIMA/006 Stationarity.en.srt 18.2 KB
04 ARIMA/015 Auto ARIMA in Code (Stocks).en.srt 17.8 KB
12 Effective Learning Strategies for Machine Learning FAQ/003 Machine Learning and AI Prerequisite Roadmap (pt 1).en.srt 17.4 KB
04 ARIMA/002 Autoregressive Models - AR(p).en.srt 17.3 KB
08 VIP_ AWS Forecast/005 Code pt 2 (Uploading the data to S3).en.srt 17.0 KB
06 Deep Learning_ Artificial Neural Networks (ANN)/007 ANN Code Preparation.en.srt 16.9 KB
04 ARIMA/014 Auto ARIMA in Code.en.srt 16.3 KB
01 Welcome/002 Where to Get the Code.en.srt 16.0 KB
02 Time Series Basics/008 Forecasting Metrics.en.srt 15.8 KB
02 Time Series Basics/009 Financial Time Series Primer.en.srt 15.6 KB
03 Exponential Smoothing and ETS Methods/011 Holt-Winters (Theory).en.srt 15.6 KB
05 Machine Learning Methods/009 Machine Learning for Time Series Forecasting in Code (pt 1).en.srt 15.5 KB
12 Effective Learning Strategies for Machine Learning FAQ/001 How to Succeed in this Course (Long Version).en.srt 15.2 KB
03 Exponential Smoothing and ETS Methods/005 EWMA Theory.en.srt 15.1 KB
03 Exponential Smoothing and ETS Methods/008 SES Code.en.srt 15.1 KB
10 Setting Up Your Environment FAQ/002 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.en.srt 14.8 KB
06 Deep Learning_ Artificial Neural Networks (ANN)/016 How Does a Neural Network _Learn__.en.srt 14.7 KB
11 Extra Help With Python Coding for Beginners FAQ/003 Proof that using Jupyter Notebook is the same as not using it.en.srt 14.6 KB
03 Exponential Smoothing and ETS Methods/007 SES Theory.en.srt 14.3 KB
04 ARIMA/004 ARIMA.en.srt 14.3 KB
06 Deep Learning_ Artificial Neural Networks (ANN)/013 Human Activity Recognition_ Multi-Input ANN.en.srt 14.0 KB
04 ARIMA/013 Model Selection, AIC and BIC.en.srt 13.9 KB
11 Extra Help With Python Coding for Beginners FAQ/002 How to Code by Yourself (part 2).en.srt 13.7 KB
05 Machine Learning Methods/006 Machine Learning Algorithms_ Support Vector Machines.en.srt 13.6 KB
04 ARIMA/008 ACF (Autocorrelation Function).en.srt 13.4 KB
08 VIP_ AWS Forecast/004 Code pt 1 (Getting and Transforming the Data).en.srt 13.3 KB
06 Deep Learning_ Artificial Neural Networks (ANN)/002 The Neuron.en.srt 13.1 KB
06 Deep Learning_ Artificial Neural Networks (ANN)/003 Forward Propagation.en.srt 12.9 KB
03 Exponential Smoothing and ETS Methods/013 Walk-Forward Validation.en.srt 12.8 KB
04 ARIMA/012 Auto ARIMA and SARIMAX.en.srt 12.7 KB
08 VIP_ AWS Forecast/002 Data Model.en.srt 12.7 KB
04 ARIMA/018 How to Forecast with ARIMA.en.srt 12.6 KB
06 Deep Learning_ Artificial Neural Networks (ANN)/004 The Geometrical Picture.en.srt 12.2 KB
06 Deep Learning_ Artificial Neural Networks (ANN)/006 Multiclass Classification.en.srt 11.5 KB
04 ARIMA/007 Stationarity in Code.en.srt 11.2 KB
06 Deep Learning_ Artificial Neural Networks (ANN)/008 Feedforward ANN for Time Series Forecasting Code.en.srt 11.2 KB
08 VIP_ AWS Forecast/001 AWS Forecast Section Introduction.en.srt 11.0 KB
04 ARIMA/017 Auto ARIMA in Code (Sales Data).en.srt 10.5 KB
05 Machine Learning Methods/003 Autoregressive Machine Learning Models.en.srt 10.5 KB
03 Exponential Smoothing and ETS Methods/009 Holt's Linear Trend Model (Theory).en.srt 10.5 KB
03 Exponential Smoothing and ETS Methods/014 Walk-Forward Validation in Code.en.srt 10.4 KB
05 Machine Learning Methods/008 Extrapolation and Stock Prices.en.srt 10.2 KB
03 Exponential Smoothing and ETS Methods/006 EWMA Code.en.srt 9.9 KB
03 Exponential Smoothing and ETS Methods/012 Holt-Winters (Code).en.srt 9.9 KB
03 Exponential Smoothing and ETS Methods/004 SMA Code.en.srt 9.8 KB
04 ARIMA/010 ACF and PACF in Code (pt 1).en.srt 9.7 KB
08 VIP_ AWS Forecast/006 Code pt 3 (Building your Model).en.srt 9.6 KB
02 Time Series Basics/012 The Naive Forecast and the Importance of Baselines.en.srt 9.6 KB
05 Machine Learning Methods/007 Machine Learning Algorithms_ Random Forest.en.srt 9.4 KB
06 Deep Learning_ Artificial Neural Networks (ANN)/009 Feedforward ANN for Stock Return and Price Predictions Code.en.srt 9.4 KB
05 Machine Learning Methods/005 Machine Learning Algorithms_ Logistic Regression.en.srt 9.3 KB
02 Time Series Basics/005 Types of Tasks.en.srt 9.2 KB
08 VIP_ AWS Forecast/007 Code pt 4 (Generating and Evaluating the Forecast).en.srt 9.0 KB
06 Deep Learning_ Artificial Neural Networks (ANN)/012 Human Activity Recognition_ Data Exploration.en.srt 8.9 KB
07 Deep Learning_ Convolutional Neural Networks (CNN)/004 What is Convolution_ (Weight Sharing).en.srt 8.9 KB
02 Time Series Basics/013 Naive Forecast and Forecasting Metrics in Code.en.srt 8.6 KB
02 Time Series Basics/006 Power, Log, and Box-Cox Transformations.en.srt 8.4 KB
04 ARIMA/011 ACF and PACF in Code (pt 2).en.srt 8.3 KB
04 ARIMA/009 PACF (Partial Autocorrelation Funtion).en.srt 8.3 KB
07 Deep Learning_ Convolutional Neural Networks (CNN)/008 CNN Code Preparation.en.srt 8.2 KB
06 Deep Learning_ Artificial Neural Networks (ANN)/011 Human Activity Recognition_ Code Preparation.en.srt 8.2 KB
13 Appendix _ FAQ Finale/002 BONUS_ Where to get discount coupons and FREE deep learning material.en.srt 8.1 KB
02 Time Series Basics/004 Why Do We Care About Shapes_.en.srt 7.9 KB
01 Welcome/001 Introduction and Outline.en.srt 7.8 KB
04 ARIMA/016 ACF and PACF for Stock Returns.en.srt 7.8 KB
06 Deep Learning_ Artificial Neural Networks (ANN)/010 Human Activity Recognition Dataset.en.srt 7.5 KB
03 Exponential Smoothing and ETS Methods/002 Exponential Smoothing Intuition for Beginners.en.srt 7.5 KB
04 ARIMA/001 ARIMA Section Introduction.en.srt 7.4 KB
07 Deep Learning_ Convolutional Neural Networks (CNN)/003 What is Convolution_ (Pattern-Matching).en.srt 7.2 KB
02 Time Series Basics/007 Power, Log, and Box-Cox Transformations in Code.en.srt 7.1 KB
08 VIP_ AWS Forecast/009 AWS Forecast Section Summary.en.srt 7.1 KB
07 Deep Learning_ Convolutional Neural Networks (CNN)/009 CNN for Time Series Forecasting in Code.en.srt 7.0 KB
05 Machine Learning Methods/011 Machine Learning for Time Series Forecasting in Code (pt 2).en.srt 6.9 KB
07 Deep Learning_ Convolutional Neural Networks (CNN)/010 CNN for Human Activity Recognition.en.srt 6.7 KB
05 Machine Learning Methods/004 Machine Learning Algorithms_ Linear Regression.en.srt 6.7 KB
07 Deep Learning_ Convolutional Neural Networks (CNN)/006 Convolution for Time Series and ARIMA.en.srt 6.7 KB
02 Time Series Basics/002 What is a Time Series_.en.srt 6.6 KB
03 Exponential Smoothing and ETS Methods/016 Application_ Stock Predictions.en.srt 6.6 KB
01 Welcome/003 Warmup (Optional).en.srt 6.3 KB
02 Time Series Basics/001 Time Series Basics Section Introduction.en.srt 6.1 KB
06 Deep Learning_ Artificial Neural Networks (ANN)/014 Human Activity Recognition_ Feature-Based Model.en.srt 5.7 KB
03 Exponential Smoothing and ETS Methods/019 Exponential Smoothing Section Summary.en.srt 5.6 KB
05 Machine Learning Methods/012 Application_ Sales Data.en.srt 5.5 KB
05 Machine Learning Methods/001 Machine Learning Section Introduction.en.srt 5.5 KB
05 Machine Learning Methods/010 Forecasting with Differencing.en.srt 5.5 KB
03 Exponential Smoothing and ETS Methods/015 Application_ Sales Data.en.srt 5.4 KB
03 Exponential Smoothing and ETS Methods/003 SMA Theory.en.srt 5.0 KB
05 Machine Learning Methods/013 Application_ Predicting Stock Prices and Returns.en.srt 5.0 KB
08 VIP_ AWS Forecast/003 Creating an IAM Role.en.srt 5.0 KB
02 Time Series Basics/015 Suggestion Box.en.srt 4.8 KB
04 ARIMA/019 ARIMA Section Summary.en.srt 4.7 KB
05 Machine Learning Methods/014 Application_ Predicting Stock Movements.en.srt 4.7 KB
06 Deep Learning_ Artificial Neural Networks (ANN)/001 Artificial Neural Networks_ Section Introduction.en.srt 4.5 KB
02 Time Series Basics/014 Time Series Basics Section Summary.en.srt 4.5 KB
03 Exponential Smoothing and ETS Methods/017 SMA Application_ COVID-19 Counting.en.srt 4.4 KB
07 Deep Learning_ Convolutional Neural Networks (CNN)/011 CNN Section Summary.en.srt 4.3 KB
04 ARIMA/003 Moving Average Models - MA(q).en.srt 4.3 KB
07 Deep Learning_ Convolutional Neural Networks (CNN)/001 CNN Section Introduction.en.srt 4.2 KB
03 Exponential Smoothing and ETS Methods/001 Exponential Smoothing Section Introduction.en.srt 4.0 KB
13 Appendix _ FAQ Finale/001 What is the Appendix_.en.srt 3.9 KB
08 VIP_ AWS Forecast/008 AWS Forecast Exercise.en.srt 3.8 KB
03 Exponential Smoothing and ETS Methods/010 Holt's Linear Trend Model (Code).en.srt 3.6 KB
02 Time Series Basics/010 Price Simulations in Code.en.srt 3.5 KB
02 Time Series Basics/003 Modeling vs. Predicting.en.srt 3.4 KB
06 Deep Learning_ Artificial Neural Networks (ANN)/015 Human Activity Recognition_ Combined Model.en.srt 3.1 KB
05 Machine Learning Methods/015 Machine Learning Section Summary.en.srt 3.1 KB
03 Exponential Smoothing and ETS Methods/018 SMA Application_ Algorithmic Trading.en.srt 3.0 KB
06 Deep Learning_ Artificial Neural Networks (ANN)/017 Artificial Neural Networks_ Section Summary.en.srt 2.9 KB
09 Extras/001 Colab Notebooks.html 977 bytes
04 ARIMA/GetFreeCourses.Co.url 116 bytes
11 Extra Help With Python Coding for Beginners FAQ/GetFreeCourses.Co.url 116 bytes
Download Paid Udemy Courses For Free.url 116 bytes
GetFreeCourses.Co.url 116 bytes
01 Welcome/external-assets-links.txt 80 bytes

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