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文档列表

04 ARIMA/005 ARIMA in Code.mp4 127.5 MB
12 Effective Learning Strategies for Machine Learning FAQ/004 Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 113.4 MB
04 ARIMA/015 Auto ARIMA in Code (Stocks).mp4 110.3 MB
04 ARIMA/014 Auto ARIMA in Code.mp4 108.2 MB
07 Deep Learning_ Convolutional Neural Networks (CNN)/007 CNN Architecture.mp4 101.5 MB
08 VIP_ AWS Forecast/005 Code pt 2 (Uploading the data to S3).mp4 95.5 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/005 Activation Functions.mp4 90.7 MB
05 Machine Learning Methods/009 Machine Learning for Time Series Forecasting in Code (pt 1).mp4 90.4 MB
12 Effective Learning Strategies for Machine Learning FAQ/003 Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 83.5 MB
07 Deep Learning_ Convolutional Neural Networks (CNN)/002 What is Convolution_.mp4 82.1 MB
07 Deep Learning_ Convolutional Neural Networks (CNN)/005 Convolution on Color Images.mp4 77.6 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/008 Feedforward ANN for Time Series Forecasting Code.mp4 74.4 MB
03 Exponential Smoothing and ETS Methods/008 SES Code.mp4 72.9 MB
11 Extra Help With Python Coding for Beginners FAQ/003 Proof that using Jupyter Notebook is the same as not using it.mp4 72.9 MB
05 Machine Learning Methods/002 Supervised Machine Learning_ Classification and Regression.mp4 72.3 MB
02 Time Series Basics/011 Random Walks and the Random Walk Hypothesis.mp4 71.4 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/009 Feedforward ANN for Stock Return and Price Predictions Code.mp4 71.0 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/013 Human Activity Recognition_ Multi-Input ANN.mp4 70.8 MB
04 ARIMA/017 Auto ARIMA in Code (Sales Data).mp4 68.6 MB
05 Machine Learning Methods/008 Extrapolation and Stock Prices.mp4 67.9 MB
08 VIP_ AWS Forecast/004 Code pt 1 (Getting and Transforming the Data).mp4 66.4 MB
01 Welcome/002 Where to Get the Code.mp4 65.0 MB
04 ARIMA/007 Stationarity in Code.mp4 64.5 MB
03 Exponential Smoothing and ETS Methods/014 Walk-Forward Validation in Code.mp4 63.2 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/007 ANN Code Preparation.mp4 60.3 MB
04 ARIMA/006 Stationarity.mp4 57.8 MB
08 VIP_ AWS Forecast/006 Code pt 3 (Building your Model).mp4 57.1 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/004 The Geometrical Picture.mp4 56.6 MB
03 Exponential Smoothing and ETS Methods/004 SMA Code.mp4 56.2 MB
04 ARIMA/002 Autoregressive Models - AR(p).mp4 55.1 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/016 How Does a Neural Network _Learn__.mp4 52.5 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/012 Human Activity Recognition_ Data Exploration.mp4 52.4 MB
08 VIP_ AWS Forecast/007 Code pt 4 (Generating and Evaluating the Forecast).mp4 52.3 MB
03 Exponential Smoothing and ETS Methods/012 Holt-Winters (Code).mp4 52.2 MB
05 Machine Learning Methods/011 Machine Learning for Time Series Forecasting in Code (pt 2).mp4 51.8 MB
11 Extra Help With Python Coding for Beginners FAQ/002 How to Code by Yourself (part 2).mp4 51.6 MB
08 VIP_ AWS Forecast/002 Data Model.mp4 51.3 MB
07 Deep Learning_ Convolutional Neural Networks (CNN)/009 CNN for Time Series Forecasting in Code.mp4 51.1 MB
03 Exponential Smoothing and ETS Methods/011 Holt-Winters (Theory).mp4 49.9 MB
07 Deep Learning_ Convolutional Neural Networks (CNN)/010 CNN for Human Activity Recognition.mp4 48.6 MB
04 ARIMA/013 Model Selection, AIC and BIC.mp4 48.1 MB
02 Time Series Basics/009 Financial Time Series Primer.mp4 47.0 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/003 Forward Propagation.mp4 47.0 MB
03 Exponential Smoothing and ETS Methods/013 Walk-Forward Validation.mp4 46.5 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/002 The Neuron.mp4 46.0 MB
02 Time Series Basics/008 Forecasting Metrics.mp4 45.8 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/006 Multiclass Classification.mp4 45.7 MB
10 Setting Up Your Environment FAQ/002 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 45.7 MB
08 VIP_ AWS Forecast/001 AWS Forecast Section Introduction.mp4 45.7 MB
05 Machine Learning Methods/006 Machine Learning Algorithms_ Support Vector Machines.mp4 45.6 MB
04 ARIMA/016 ACF and PACF for Stock Returns.mp4 45.6 MB
05 Machine Learning Methods/012 Application_ Sales Data.mp4 44.2 MB
02 Time Series Basics/013 Naive Forecast and Forecasting Metrics in Code.mp4 43.5 MB
04 ARIMA/004 ARIMA.mp4 43.4 MB
04 ARIMA/010 ACF and PACF in Code (pt 1).mp4 43.3 MB
03 Exponential Smoothing and ETS Methods/016 Application_ Stock Predictions.mp4 42.5 MB
04 ARIMA/012 Auto ARIMA and SARIMAX.mp4 41.4 MB
03 Exponential Smoothing and ETS Methods/006 EWMA Code.mp4 41.3 MB
12 Effective Learning Strategies for Machine Learning FAQ/002 Is this for Beginners or Experts_ Academic or Practical_ Fast or slow-paced_.mp4 40.8 MB
04 ARIMA/018 How to Forecast with ARIMA.mp4 39.8 MB
13 Appendix _ FAQ Finale/002 BONUS_ Where to get discount coupons and FREE deep learning material.mp4 39.6 MB
05 Machine Learning Methods/013 Application_ Predicting Stock Prices and Returns.mp4 39.2 MB
04 ARIMA/008 ACF (Autocorrelation Function).mp4 38.8 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/014 Human Activity Recognition_ Feature-Based Model.mp4 37.8 MB
03 Exponential Smoothing and ETS Methods/005 EWMA Theory.mp4 37.6 MB
03 Exponential Smoothing and ETS Methods/007 SES Theory.mp4 37.3 MB
04 ARIMA/011 ACF and PACF in Code (pt 2).mp4 35.5 MB
02 Time Series Basics/007 Power, Log, and Box-Cox Transformations in Code.mp4 34.9 MB
03 Exponential Smoothing and ETS Methods/009 Holt's Linear Trend Model (Theory).mp4 34.8 MB
02 Time Series Basics/006 Power, Log, and Box-Cox Transformations.mp4 34.2 MB
05 Machine Learning Methods/003 Autoregressive Machine Learning Models.mp4 34.0 MB
02 Time Series Basics/002 What is a Time Series_.mp4 33.8 MB
05 Machine Learning Methods/007 Machine Learning Algorithms_ Random Forest.mp4 33.6 MB
05 Machine Learning Methods/005 Machine Learning Algorithms_ Logistic Regression.mp4 33.3 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/011 Human Activity Recognition_ Code Preparation.mp4 32.8 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/010 Human Activity Recognition Dataset.mp4 32.2 MB
01 Welcome/001 Introduction and Outline.mp4 32.2 MB
07 Deep Learning_ Convolutional Neural Networks (CNN)/004 What is Convolution_ (Weight Sharing).mp4 31.9 MB
02 Time Series Basics/012 The Naive Forecast and the Importance of Baselines.mp4 31.6 MB
02 Time Series Basics/004 Why Do We Care About Shapes_.mp4 30.9 MB
03 Exponential Smoothing and ETS Methods/015 Application_ Sales Data.mp4 30.9 MB
10 Setting Up Your Environment FAQ/001 Anaconda Environment Setup.mp4 29.2 MB
07 Deep Learning_ Convolutional Neural Networks (CNN)/008 CNN Code Preparation.mp4 28.8 MB
05 Machine Learning Methods/014 Application_ Predicting Stock Movements.mp4 27.6 MB
08 VIP_ AWS Forecast/009 AWS Forecast Section Summary.mp4 26.7 MB
04 ARIMA/009 PACF (Partial Autocorrelation Funtion).mp4 26.3 MB
11 Extra Help With Python Coding for Beginners FAQ/001 How to Code by Yourself (part 1).mp4 25.8 MB
03 Exponential Smoothing and ETS Methods/002 Exponential Smoothing Intuition for Beginners.mp4 25.1 MB
08 VIP_ AWS Forecast/003 Creating an IAM Role.mp4 25.0 MB
07 Deep Learning_ Convolutional Neural Networks (CNN)/003 What is Convolution_ (Pattern-Matching).mp4 24.8 MB
07 Deep Learning_ Convolutional Neural Networks (CNN)/006 Convolution for Time Series and ARIMA.mp4 24.8 MB
02 Time Series Basics/005 Types of Tasks.mp4 24.7 MB
01 Welcome/003 Warmup (Optional).mp4 24.3 MB
04 ARIMA/001 ARIMA Section Introduction.mp4 24.1 MB
05 Machine Learning Methods/004 Machine Learning Algorithms_ Linear Regression.mp4 22.9 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/015 Human Activity Recognition_ Combined Model.mp4 21.9 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/001 Artificial Neural Networks_ Section Introduction.mp4 20.4 MB
03 Exponential Smoothing and ETS Methods/017 SMA Application_ COVID-19 Counting.mp4 20.3 MB
03 Exponential Smoothing and ETS Methods/019 Exponential Smoothing Section Summary.mp4 20.0 MB
03 Exponential Smoothing and ETS Methods/010 Holt's Linear Trend Model (Code).mp4 20.0 MB
05 Machine Learning Methods/010 Forecasting with Differencing.mp4 19.9 MB
02 Time Series Basics/010 Price Simulations in Code.mp4 19.2 MB
05 Machine Learning Methods/001 Machine Learning Section Introduction.mp4 18.4 MB
02 Time Series Basics/001 Time Series Basics Section Introduction.mp4 18.3 MB
13 Appendix _ FAQ Finale/001 What is the Appendix_.mp4 17.2 MB
02 Time Series Basics/015 Suggestion Box.mp4 16.9 MB
07 Deep Learning_ Convolutional Neural Networks (CNN)/011 CNN Section Summary.mp4 16.2 MB
03 Exponential Smoothing and ETS Methods/003 SMA Theory.mp4 16.0 MB
07 Deep Learning_ Convolutional Neural Networks (CNN)/001 CNN Section Introduction.mp4 15.0 MB
08 VIP_ AWS Forecast/008 AWS Forecast Exercise.mp4 14.4 MB
03 Exponential Smoothing and ETS Methods/001 Exponential Smoothing Section Introduction.mp4 14.2 MB
02 Time Series Basics/003 Modeling vs. Predicting.mp4 14.1 MB
04 ARIMA/019 ARIMA Section Summary.mp4 13.4 MB
12 Effective Learning Strategies for Machine Learning FAQ/001 How to Succeed in this Course (Long Version).mp4 13.2 MB
02 Time Series Basics/014 Time Series Basics Section Summary.mp4 12.7 MB
03 Exponential Smoothing and ETS Methods/018 SMA Application_ Algorithmic Trading.mp4 12.2 MB
06 Deep Learning_ Artificial Neural Networks (ANN)/017 Artificial Neural Networks_ Section Summary.mp4 11.5 MB
05 Machine Learning Methods/015 Machine Learning Section Summary.mp4 10.9 MB
04 ARIMA/003 Moving Average Models - MA(q).mp4 10.6 MB
07 Deep Learning_ Convolutional Neural Networks (CNN)/007 CNN Architecture.en.srt 34.0 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 33.8 kB
12 Effective Learning Strategies for Machine Learning FAQ/004 Machine Learning and AI Prerequisite Roadmap (pt 2).en.srt 25.0 kB
04 ARIMA/005 ARIMA in Code.en.srt 24.3 kB
06 Deep Learning_ Artificial Neural Networks (ANN)/005 Activation Functions.en.srt 24.3 kB
11 Extra Help With Python Coding for Beginners FAQ/001 How to Code by Yourself (part 1).en.srt 24.0 kB
07 Deep Learning_ Convolutional Neural Networks (CNN)/005 Convolution on Color Images.en.srt 22.1 kB
07 Deep Learning_ Convolutional Neural Networks (CNN)/002 What is Convolution_.en.srt 22.0 kB
10 Setting Up Your Environment FAQ/001 Anaconda Environment Setup.en.srt 21.6 kB
02 Time Series Basics/011 Random Walks and the Random Walk Hypothesis.en.srt 20.6 kB
05 Machine Learning Methods/002 Supervised Machine Learning_ Classification and Regression.en.srt 20.1 kB
04 ARIMA/006 Stationarity.en.srt 18.6 kB
04 ARIMA/015 Auto ARIMA in Code (Stocks).en.srt 18.2 kB
12 Effective Learning Strategies for Machine Learning FAQ/003 Machine Learning and AI Prerequisite Roadmap (pt 1).en.srt 17.8 kB
04 ARIMA/002 Autoregressive Models - AR(p).en.srt 17.7 kB
08 VIP_ AWS Forecast/005 Code pt 2 (Uploading the data to S3).en.srt 17.5 kB
06 Deep Learning_ Artificial Neural Networks (ANN)/007 ANN Code Preparation.en.srt 17.3 kB
04 ARIMA/014 Auto ARIMA in Code.en.srt 16.7 kB
01 Welcome/002 Where to Get the Code.en.srt 16.4 kB
02 Time Series Basics/008 Forecasting Metrics.en.srt 16.2 kB
02 Time Series Basics/009 Financial Time Series Primer.en.srt 15.9 kB
03 Exponential Smoothing and ETS Methods/011 Holt-Winters (Theory).en.srt 15.9 kB
05 Machine Learning Methods/009 Machine Learning for Time Series Forecasting in Code (pt 1).en.srt 15.9 kB
12 Effective Learning Strategies for Machine Learning FAQ/001 How to Succeed in this Course (Long Version).en.srt 15.5 kB
03 Exponential Smoothing and ETS Methods/005 EWMA Theory.en.srt 15.5 kB
03 Exponential Smoothing and ETS Methods/008 SES Code.en.srt 15.5 kB
10 Setting Up Your Environment FAQ/002 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.en.srt 15.2 kB
06 Deep Learning_ Artificial Neural Networks (ANN)/016 How Does a Neural Network _Learn__.en.srt 15.1 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 14.9 kB
03 Exponential Smoothing and ETS Methods/007 SES Theory.en.srt 14.7 kB
04 ARIMA/004 ARIMA.en.srt 14.7 kB
06 Deep Learning_ Artificial Neural Networks (ANN)/013 Human Activity Recognition_ Multi-Input ANN.en.srt 14.3 kB
04 ARIMA/013 Model Selection, AIC and BIC.en.srt 14.3 kB
11 Extra Help With Python Coding for Beginners FAQ/002 How to Code by Yourself (part 2).en.srt 14.0 kB
05 Machine Learning Methods/006 Machine Learning Algorithms_ Support Vector Machines.en.srt 14.0 kB
04 ARIMA/008 ACF (Autocorrelation Function).en.srt 13.8 kB
08 VIP_ AWS Forecast/004 Code pt 1 (Getting and Transforming the Data).en.srt 13.7 kB
06 Deep Learning_ Artificial Neural Networks (ANN)/002 The Neuron.en.srt 13.4 kB
06 Deep Learning_ Artificial Neural Networks (ANN)/003 Forward Propagation.en.srt 13.2 kB
03 Exponential Smoothing and ETS Methods/013 Walk-Forward Validation.en.srt 13.1 kB
04 ARIMA/012 Auto ARIMA and SARIMAX.en.srt 13.0 kB
08 VIP_ AWS Forecast/002 Data Model.en.srt 13.0 kB
04 ARIMA/018 How to Forecast with ARIMA.en.srt 12.9 kB
06 Deep Learning_ Artificial Neural Networks (ANN)/004 The Geometrical Picture.en.srt 12.5 kB
06 Deep Learning_ Artificial Neural Networks (ANN)/006 Multiclass Classification.en.srt 11.8 kB
04 ARIMA/007 Stationarity in Code.en.srt 11.4 kB
06 Deep Learning_ Artificial Neural Networks (ANN)/008 Feedforward ANN for Time Series Forecasting Code.en.srt 11.4 kB
08 VIP_ AWS Forecast/001 AWS Forecast Section Introduction.en.srt 11.3 kB
04 ARIMA/017 Auto ARIMA in Code (Sales Data).en.srt 10.8 kB
05 Machine Learning Methods/003 Autoregressive Machine Learning Models.en.srt 10.8 kB
03 Exponential Smoothing and ETS Methods/009 Holt's Linear Trend Model (Theory).en.srt 10.7 kB
03 Exponential Smoothing and ETS Methods/014 Walk-Forward Validation in Code.en.srt 10.7 kB
05 Machine Learning Methods/008 Extrapolation and Stock Prices.en.srt 10.4 kB
03 Exponential Smoothing and ETS Methods/006 EWMA Code.en.srt 10.2 kB
03 Exponential Smoothing and ETS Methods/012 Holt-Winters (Code).en.srt 10.2 kB
03 Exponential Smoothing and ETS Methods/004 SMA Code.en.srt 10.1 kB
04 ARIMA/010 ACF and PACF in Code (pt 1).en.srt 9.9 kB
08 VIP_ AWS Forecast/006 Code pt 3 (Building your Model).en.srt 9.8 kB
02 Time Series Basics/012 The Naive Forecast and the Importance of Baselines.en.srt 9.8 kB
05 Machine Learning Methods/007 Machine Learning Algorithms_ Random Forest.en.srt 9.6 kB
06 Deep Learning_ Artificial Neural Networks (ANN)/009 Feedforward ANN for Stock Return and Price Predictions Code.en.srt 9.6 kB
05 Machine Learning Methods/005 Machine Learning Algorithms_ Logistic Regression.en.srt 9.6 kB
02 Time Series Basics/005 Types of Tasks.en.srt 9.5 kB
08 VIP_ AWS Forecast/007 Code pt 4 (Generating and Evaluating the Forecast).en.srt 9.2 kB
06 Deep Learning_ Artificial Neural Networks (ANN)/012 Human Activity Recognition_ Data Exploration.en.srt 9.1 kB
07 Deep Learning_ Convolutional Neural Networks (CNN)/004 What is Convolution_ (Weight Sharing).en.srt 9.1 kB
02 Time Series Basics/013 Naive Forecast and Forecasting Metrics in Code.en.srt 8.8 kB
02 Time Series Basics/006 Power, Log, and Box-Cox Transformations.en.srt 8.6 kB
04 ARIMA/011 ACF and PACF in Code (pt 2).en.srt 8.5 kB
04 ARIMA/009 PACF (Partial Autocorrelation Funtion).en.srt 8.5 kB
07 Deep Learning_ Convolutional Neural Networks (CNN)/008 CNN Code Preparation.en.srt 8.4 kB
06 Deep Learning_ Artificial Neural Networks (ANN)/011 Human Activity Recognition_ Code Preparation.en.srt 8.4 kB
13 Appendix _ FAQ Finale/002 BONUS_ Where to get discount coupons and FREE deep learning material.en.srt 8.3 kB
02 Time Series Basics/004 Why Do We Care About Shapes_.en.srt 8.1 kB
01 Welcome/001 Introduction and Outline.en.srt 8.0 kB
04 ARIMA/016 ACF and PACF for Stock Returns.en.srt 8.0 kB
06 Deep Learning_ Artificial Neural Networks (ANN)/010 Human Activity Recognition Dataset.en.srt 7.7 kB
03 Exponential Smoothing and ETS Methods/002 Exponential Smoothing Intuition for Beginners.en.srt 7.7 kB
04 ARIMA/001 ARIMA Section Introduction.en.srt 7.6 kB
07 Deep Learning_ Convolutional Neural Networks (CNN)/003 What is Convolution_ (Pattern-Matching).en.srt 7.3 kB
02 Time Series Basics/007 Power, Log, and Box-Cox Transformations in Code.en.srt 7.2 kB
08 VIP_ AWS Forecast/009 AWS Forecast Section Summary.en.srt 7.2 kB
07 Deep Learning_ Convolutional Neural Networks (CNN)/009 CNN for Time Series Forecasting in Code.en.srt 7.2 kB
05 Machine Learning Methods/011 Machine Learning for Time Series Forecasting in Code (pt 2).en.srt 7.1 kB
07 Deep Learning_ Convolutional Neural Networks (CNN)/010 CNN for Human Activity Recognition.en.srt 6.8 kB
05 Machine Learning Methods/004 Machine Learning Algorithms_ Linear Regression.en.srt 6.8 kB
07 Deep Learning_ Convolutional Neural Networks (CNN)/006 Convolution for Time Series and ARIMA.en.srt 6.8 kB
02 Time Series Basics/002 What is a Time Series_.en.srt 6.7 kB
03 Exponential Smoothing and ETS Methods/016 Application_ Stock Predictions.en.srt 6.7 kB
01 Welcome/003 Warmup (Optional).en.srt 6.4 kB
02 Time Series Basics/001 Time Series Basics Section Introduction.en.srt 6.2 kB
06 Deep Learning_ Artificial Neural Networks (ANN)/014 Human Activity Recognition_ Feature-Based Model.en.srt 5.9 kB
03 Exponential Smoothing and ETS Methods/019 Exponential Smoothing Section Summary.en.srt 5.7 kB
05 Machine Learning Methods/012 Application_ Sales Data.en.srt 5.7 kB
05 Machine Learning Methods/001 Machine Learning Section Introduction.en.srt 5.7 kB
05 Machine Learning Methods/010 Forecasting with Differencing.en.srt 5.6 kB
03 Exponential Smoothing and ETS Methods/015 Application_ Sales Data.en.srt 5.5 kB
03 Exponential Smoothing and ETS Methods/003 SMA Theory.en.srt 5.1 kB
05 Machine Learning Methods/013 Application_ Predicting Stock Prices and Returns.en.srt 5.1 kB
08 VIP_ AWS Forecast/003 Creating an IAM Role.en.srt 5.1 kB
02 Time Series Basics/015 Suggestion Box.en.srt 5.0 kB
04 ARIMA/019 ARIMA Section Summary.en.srt 4.8 kB
05 Machine Learning Methods/014 Application_ Predicting Stock Movements.en.srt 4.8 kB
06 Deep Learning_ Artificial Neural Networks (ANN)/001 Artificial Neural Networks_ Section Introduction.en.srt 4.6 kB
02 Time Series Basics/014 Time Series Basics Section Summary.en.srt 4.6 kB
03 Exponential Smoothing and ETS Methods/017 SMA Application_ COVID-19 Counting.en.srt 4.5 kB
07 Deep Learning_ Convolutional Neural Networks (CNN)/011 CNN Section Summary.en.srt 4.4 kB
04 ARIMA/003 Moving Average Models - MA(q).en.srt 4.4 kB
07 Deep Learning_ Convolutional Neural Networks (CNN)/001 CNN Section Introduction.en.srt 4.3 kB
03 Exponential Smoothing and ETS Methods/001 Exponential Smoothing Section Introduction.en.srt 4.1 kB
13 Appendix _ FAQ Finale/001 What is the Appendix_.en.srt 4.0 kB
08 VIP_ AWS Forecast/008 AWS Forecast Exercise.en.srt 3.9 kB
03 Exponential Smoothing and ETS Methods/010 Holt's Linear Trend Model (Code).en.srt 3.7 kB
02 Time Series Basics/010 Price Simulations in Code.en.srt 3.6 kB
02 Time Series Basics/003 Modeling vs. Predicting.en.srt 3.5 kB
06 Deep Learning_ Artificial Neural Networks (ANN)/015 Human Activity Recognition_ Combined Model.en.srt 3.2 kB
05 Machine Learning Methods/015 Machine Learning Section Summary.en.srt 3.2 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 3.0 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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