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| [CourseClub.Me].url |
122б |
| [FCS Forum].url |
133б |
| [FreeCourseSite.com].url |
127б |
| [GigaCourse.Com].url |
49б |
| 001 Anaconda Environment Setup.en.srt |
21.09Кб |
| 001 Anaconda Environment Setup.mp4 |
27.88Мб |
| 001 ARIMA Section Introduction.en.srt |
7.41Кб |
| 001 ARIMA Section Introduction.mp4 |
23.01Мб |
| 001 Artificial Neural Networks_ Section Introduction.en.srt |
4.53Кб |
| 001 Artificial Neural Networks_ Section Introduction.mp4 |
19.43Мб |
| 001 AWS Forecast Section Introduction.en.srt |
11.02Кб |
| 001 AWS Forecast Section Introduction.mp4 |
43.54Мб |
| 001 CNN Section Introduction.en.srt |
4.20Кб |
| 001 CNN Section Introduction.mp4 |
14.31Мб |
| 001 Colab Notebooks.html |
977б |
| 001 Exponential Smoothing Section Introduction.en.srt |
4.01Кб |
| 001 Exponential Smoothing Section Introduction.mp4 |
13.56Мб |
| 001 How to Code by Yourself (part 1).en.srt |
23.46Кб |
| 001 How to Code by Yourself (part 1).mp4 |
24.59Мб |
| 001 How to Succeed in this Course (Long Version).en.srt |
15.17Кб |
| 001 How to Succeed in this Course (Long Version).mp4 |
12.60Мб |
| 001 Introduction and Outline.en.srt |
7.78Кб |
| 001 Introduction and Outline.mp4 |
30.69Мб |
| 001 Machine Learning Section Introduction.en.srt |
5.52Кб |
| 001 Machine Learning Section Introduction.mp4 |
17.53Мб |
| 001 Time Series Basics Section Introduction.en.srt |
6.08Кб |
| 001 Time Series Basics Section Introduction.mp4 |
17.46Мб |
| 001 What is the Appendix_.en.srt |
3.91Кб |
| 001 What is the Appendix_.mp4 |
16.40Мб |
| 002 Autoregressive Models - AR(p).en.srt |
17.29Кб |
| 002 Autoregressive Models - AR(p).mp4 |
52.54Мб |
| 002 BONUS_ Where to get discount coupons and FREE deep learning material.en.srt |
8.13Кб |
| 002 BONUS_ Where to get discount coupons and FREE deep learning material.mp4 |
37.81Мб |
| 002 Data Model.en.srt |
12.68Кб |
| 002 Data Model.mp4 |
48.96Мб |
| 002 Exponential Smoothing Intuition for Beginners.en.srt |
7.50Кб |
| 002 Exponential Smoothing Intuition for Beginners.mp4 |
23.91Мб |
| 002 How to Code by Yourself (part 2).en.srt |
13.66Кб |
| 002 How to Code by Yourself (part 2).mp4 |
49.18Мб |
| 002 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.en.srt |
14.82Кб |
| 002 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 |
43.61Мб |
| 002 Is this for Beginners or Experts_ Academic or Practical_ Fast or slow-paced_.en.srt |
33.02Кб |
| 002 Is this for Beginners or Experts_ Academic or Practical_ Fast or slow-paced_.mp4 |
38.95Мб |
| 002 Supervised Machine Learning_ Classification and Regression.en.srt |
19.65Кб |
| 002 Supervised Machine Learning_ Classification and Regression.mp4 |
68.96Мб |
| 002 The Neuron.en.srt |
13.13Кб |
| 002 The Neuron.mp4 |
43.86Мб |
| 002 What is a Time Series_.en.srt |
6.57Кб |
| 002 What is a Time Series_.mp4 |
32.24Мб |
| 002 What is Convolution_.en.srt |
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| 002 What is Convolution_.mp4 |
78.29Мб |
| 002 Where to Get the Code.en.srt |
16.03Кб |
| 002 Where to Get the Code.mp4 |
61.97Мб |
| 003 Autoregressive Machine Learning Models.en.srt |
10.52Кб |
| 003 Autoregressive Machine Learning Models.mp4 |
32.38Мб |
| 003 Creating an IAM Role.en.srt |
4.98Кб |
| 003 Creating an IAM Role.mp4 |
23.80Мб |
| 003 Forward Propagation.en.srt |
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| 003 Forward Propagation.mp4 |
44.79Мб |
| 003 Machine Learning and AI Prerequisite Roadmap (pt 1).en.srt |
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| 003 Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 |
79.62Мб |
| 003 Modeling vs. Predicting.en.srt |
3.41Кб |
| 003 Modeling vs. Predicting.mp4 |
13.48Мб |
| 003 Moving Average Models - MA(q).en.srt |
4.31Кб |
| 003 Moving Average Models - MA(q).mp4 |
10.13Мб |
| 003 Proof that using Jupyter Notebook is the same as not using it.en.srt |
14.60Кб |
| 003 Proof that using Jupyter Notebook is the same as not using it.mp4 |
69.51Мб |
| 003 SMA Theory.en.srt |
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| 003 SMA Theory.mp4 |
15.24Мб |
| 003 Warmup (Optional).en.srt |
6.28Кб |
| 003 Warmup (Optional).mp4 |
23.16Мб |
| 003 What is Convolution_ (Pattern-Matching).en.srt |
7.17Кб |
| 003 What is Convolution_ (Pattern-Matching).mp4 |
23.69Мб |
| 004 ARIMA.en.srt |
14.32Кб |
| 004 ARIMA.mp4 |
41.39Мб |
| 004 Code pt 1 (Getting and Transforming the Data).en.srt |
13.34Кб |
| 004 Code pt 1 (Getting and Transforming the Data).mp4 |
63.34Мб |
| 004 Machine Learning Algorithms_ Linear Regression.en.srt |
6.68Кб |
| 004 Machine Learning Algorithms_ Linear Regression.mp4 |
21.80Мб |
| 004 Machine Learning and AI Prerequisite Roadmap (pt 2).en.srt |
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| 004 Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 |
108.19Мб |
| 004 SMA Code.en.srt |
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| 004 SMA Code.mp4 |
53.57Мб |
| 004 The Geometrical Picture.en.srt |
12.17Кб |
| 004 The Geometrical Picture.mp4 |
53.97Мб |
| 004 What is Convolution_ (Weight Sharing).en.srt |
8.88Кб |
| 004 What is Convolution_ (Weight Sharing).mp4 |
30.44Мб |
| 004 Why Do We Care About Shapes_.en.srt |
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| 004 Why Do We Care About Shapes_.mp4 |
29.48Мб |
| 005 Activation Functions.en.srt |
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| 005 Activation Functions.mp4 |
86.54Мб |
| 005 ARIMA in Code.en.srt |
23.73Кб |
| 005 ARIMA in Code.mp4 |
121.58Мб |
| 005 Code pt 2 (Uploading the data to S3).en.srt |
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| 005 Code pt 2 (Uploading the data to S3).mp4 |
91.06Мб |
| 005 Convolution on Color Images.en.srt |
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| 005 Convolution on Color Images.mp4 |
73.99Мб |
| 005 EWMA Theory.en.srt |
15.14Кб |
| 005 EWMA Theory.mp4 |
35.83Мб |
| 005 Machine Learning Algorithms_ Logistic Regression.en.srt |
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| 005 Machine Learning Algorithms_ Logistic Regression.mp4 |
31.74Мб |
| 005 Types of Tasks.en.srt |
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| 005 Types of Tasks.mp4 |
23.55Мб |
| 006 Code pt 3 (Building your Model).en.srt |
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| 006 Code pt 3 (Building your Model).mp4 |
54.47Мб |
| 006 Convolution for Time Series and ARIMA.en.srt |
6.65Кб |
| 006 Convolution for Time Series and ARIMA.mp4 |
23.61Мб |
| 006 EWMA Code.en.srt |
9.93Кб |
| 006 EWMA Code.mp4 |
39.41Мб |
| 006 Machine Learning Algorithms_ Support Vector Machines.en.srt |
13.63Кб |
| 006 Machine Learning Algorithms_ Support Vector Machines.mp4 |
43.52Мб |
| 006 Multiclass Classification.en.srt |
11.51Кб |
| 006 Multiclass Classification.mp4 |
43.63Мб |
| 006 Power, Log, and Box-Cox Transformations.en.srt |
8.41Кб |
| 006 Power, Log, and Box-Cox Transformations.mp4 |
32.63Мб |
| 006 Stationarity.en.srt |
18.20Кб |
| 006 Stationarity.mp4 |
55.15Мб |
| 007 ANN Code Preparation.en.srt |
16.88Кб |
| 007 ANN Code Preparation.mp4 |
57.51Мб |
| 007 CNN Architecture.en.srt |
33.24Кб |
| 007 CNN Architecture.mp4 |
96.82Мб |
| 007 Code pt 4 (Generating and Evaluating the Forecast).en.srt |
8.96Кб |
| 007 Code pt 4 (Generating and Evaluating the Forecast).mp4 |
49.88Мб |
| 007 Machine Learning Algorithms_ Random Forest.en.srt |
9.40Кб |
| 007 Machine Learning Algorithms_ Random Forest.mp4 |
32.02Мб |
| 007 Power, Log, and Box-Cox Transformations in Code.en.srt |
7.07Кб |
| 007 Power, Log, and Box-Cox Transformations in Code.mp4 |
33.29Мб |
| 007 SES Theory.en.srt |
14.35Кб |
| 007 SES Theory.mp4 |
35.57Мб |
| 007 Stationarity in Code.en.srt |
11.17Кб |
| 007 Stationarity in Code.mp4 |
61.50Мб |
| 008 ACF (Autocorrelation Function).en.srt |
13.44Кб |
| 008 ACF (Autocorrelation Function).mp4 |
37.00Мб |
| 008 AWS Forecast Exercise.en.srt |
3.79Кб |
| 008 AWS Forecast Exercise.mp4 |
13.76Мб |
| 008 CNN Code Preparation.en.srt |
8.22Кб |
| 008 CNN Code Preparation.mp4 |
27.49Мб |
| 008 Extrapolation and Stock Prices.en.srt |
10.16Кб |
| 008 Extrapolation and Stock Prices.mp4 |
64.73Мб |
| 008 Feedforward ANN for Time Series Forecasting Code.en.srt |
11.16Кб |
| 008 Feedforward ANN for Time Series Forecasting Code.mp4 |
70.91Мб |
| 008 Forecasting Metrics.en.srt |
15.80Кб |
| 008 Forecasting Metrics.mp4 |
43.71Мб |
| 008 SES Code.en.srt |
15.09Кб |
| 008 SES Code.mp4 |
69.54Мб |
| 009 AWS Forecast Section Summary.en.srt |
7.06Кб |
| 009 AWS Forecast Section Summary.mp4 |
25.46Мб |
| 009 CNN for Time Series Forecasting in Code.en.srt |
7.04Кб |
| 009 CNN for Time Series Forecasting in Code.mp4 |
48.78Мб |
| 009 Feedforward ANN for Stock Return and Price Predictions Code.en.srt |
9.37Кб |
| 009 Feedforward ANN for Stock Return and Price Predictions Code.mp4 |
67.71Мб |
| 009 Financial Time Series Primer.en.srt |
15.56Кб |
| 009 Financial Time Series Primer.mp4 |
44.86Мб |
| 009 Holt's Linear Trend Model (Theory).en.srt |
10.45Кб |
| 009 Holt's Linear Trend Model (Theory).mp4 |
33.20Мб |
| 009 Machine Learning for Time Series Forecasting in Code (pt 1).en.srt |
15.51Кб |
| 009 Machine Learning for Time Series Forecasting in Code (pt 1).mp4 |
86.17Мб |
| 009 PACF (Partial Autocorrelation Funtion).en.srt |
8.26Кб |
| 009 PACF (Partial Autocorrelation Funtion).mp4 |
25.11Мб |
| 010 ACF and PACF in Code (pt 1).en.srt |
9.68Кб |
| 010 ACF and PACF in Code (pt 1).mp4 |
41.31Мб |
| 010 CNN for Human Activity Recognition.en.srt |
6.68Кб |
| 010 CNN for Human Activity Recognition.mp4 |
46.39Мб |
| 010 Forecasting with Differencing.en.srt |
5.49Кб |
| 010 Forecasting with Differencing.mp4 |
18.97Мб |
| 010 Holt's Linear Trend Model (Code).en.srt |
3.57Кб |
| 010 Holt's Linear Trend Model (Code).mp4 |
19.05Мб |
| 010 Human Activity Recognition Dataset.en.srt |
7.52Кб |
| 010 Human Activity Recognition Dataset.mp4 |
30.74Мб |
| 010 Price Simulations in Code.en.srt |
3.54Кб |
| 010 Price Simulations in Code.mp4 |
18.28Мб |
| 011 ACF and PACF in Code (pt 2).en.srt |
8.31Кб |
| 011 ACF and PACF in Code (pt 2).mp4 |
33.88Мб |
| 011 CNN Section Summary.en.srt |
4.31Кб |
| 011 CNN Section Summary.mp4 |
15.43Мб |
| 011 Holt-Winters (Theory).en.srt |
15.55Кб |
| 011 Holt-Winters (Theory).mp4 |
47.55Мб |
| 011 Human Activity Recognition_ Code Preparation.en.srt |
8.20Кб |
| 011 Human Activity Recognition_ Code Preparation.mp4 |
31.27Мб |
| 011 Machine Learning for Time Series Forecasting in Code (pt 2).en.srt |
6.90Кб |
| 011 Machine Learning for Time Series Forecasting in Code (pt 2).mp4 |
49.40Мб |
| 011 Random Walks and the Random Walk Hypothesis.en.srt |
20.09Кб |
| 011 Random Walks and the Random Walk Hypothesis.mp4 |
68.11Мб |
| 012 Application_ Sales Data.en.srt |
5.54Кб |
| 012 Application_ Sales Data.mp4 |
42.19Мб |
| 012 Auto ARIMA and SARIMAX.en.srt |
12.72Кб |
| 012 Auto ARIMA and SARIMAX.mp4 |
39.45Мб |
| 012 Holt-Winters (Code).en.srt |
9.92Кб |
| 012 Holt-Winters (Code).mp4 |
49.80Мб |
| 012 Human Activity Recognition_ Data Exploration.en.srt |
8.90Кб |
| 012 Human Activity Recognition_ Data Exploration.mp4 |
49.95Мб |
| 012 The Naive Forecast and the Importance of Baselines.en.srt |
9.57Кб |
| 012 The Naive Forecast and the Importance of Baselines.mp4 |
30.11Мб |
| 013 Application_ Predicting Stock Prices and Returns.en.srt |
5.02Кб |
| 013 Application_ Predicting Stock Prices and Returns.mp4 |
37.36Мб |
| 013 Human Activity Recognition_ Multi-Input ANN.en.srt |
13.96Кб |
| 013 Human Activity Recognition_ Multi-Input ANN.mp4 |
67.55Мб |
| 013 Model Selection, AIC and BIC.en.srt |
13.94Кб |
| 013 Model Selection, AIC and BIC.mp4 |
45.91Мб |
| 013 Naive Forecast and Forecasting Metrics in Code.en.srt |
8.58Кб |
| 013 Naive Forecast and Forecasting Metrics in Code.mp4 |
41.47Мб |
| 013 Walk-Forward Validation.en.srt |
12.78Кб |
| 013 Walk-Forward Validation.mp4 |
44.31Мб |
| 014 Application_ Predicting Stock Movements.en.srt |
4.66Кб |
| 014 Application_ Predicting Stock Movements.mp4 |
26.28Мб |
| 014 Auto ARIMA in Code.en.srt |
16.34Кб |
| 014 Auto ARIMA in Code.mp4 |
103.19Мб |
| 014 Human Activity Recognition_ Feature-Based Model.en.srt |
5.73Кб |
| 014 Human Activity Recognition_ Feature-Based Model.mp4 |
36.06Мб |
| 014 Time Series Basics Section Summary.en.srt |
4.49Кб |
| 014 Time Series Basics Section Summary.mp4 |
12.13Мб |
| 014 Walk-Forward Validation in Code.en.srt |
10.41Кб |
| 014 Walk-Forward Validation in Code.mp4 |
60.25Мб |
| 015 Application_ Sales Data.en.srt |
5.40Кб |
| 015 Application_ Sales Data.mp4 |
29.44Мб |
| 015 Auto ARIMA in Code (Stocks).en.srt |
17.75Кб |
| 015 Auto ARIMA in Code (Stocks).mp4 |
105.21Мб |
| 015 Human Activity Recognition_ Combined Model.en.srt |
3.14Кб |
| 015 Human Activity Recognition_ Combined Model.mp4 |
20.90Мб |
| 015 Machine Learning Section Summary.en.srt |
3.12Кб |
| 015 Machine Learning Section Summary.mp4 |
10.36Мб |
| 015 Suggestion Box.en.srt |
4.85Кб |
| 015 Suggestion Box.mp4 |
16.12Мб |
| 016 ACF and PACF for Stock Returns.en.srt |
7.77Кб |
| 016 ACF and PACF for Stock Returns.mp4 |
43.50Мб |
| 016 Application_ Stock Predictions.en.srt |
6.56Кб |
| 016 Application_ Stock Predictions.mp4 |
40.51Мб |
| 016 How Does a Neural Network _Learn__.en.srt |
14.70Кб |
| 016 How Does a Neural Network _Learn__.mp4 |
50.07Мб |
| 017 Artificial Neural Networks_ Section Summary.en.srt |
2.92Кб |
| 017 Artificial Neural Networks_ Section Summary.mp4 |
10.95Мб |
| 017 Auto ARIMA in Code (Sales Data).en.srt |
10.54Кб |
| 017 Auto ARIMA in Code (Sales Data).mp4 |
65.42Мб |
| 017 SMA Application_ COVID-19 Counting.en.srt |
4.37Кб |
| 017 SMA Application_ COVID-19 Counting.mp4 |
19.37Мб |
| 018 How to Forecast with ARIMA.en.srt |
12.62Кб |
| 018 How to Forecast with ARIMA.mp4 |
37.95Мб |
| 018 SMA Application_ Algorithmic Trading.en.srt |
2.96Кб |
| 018 SMA Application_ Algorithmic Trading.mp4 |
11.59Мб |
| 019 ARIMA Section Summary.en.srt |
4.73Кб |
| 019 ARIMA Section Summary.mp4 |
12.74Мб |
| 019 Exponential Smoothing Section Summary.en.srt |
5.61Кб |
| 019 Exponential Smoothing Section Summary.mp4 |
19.12Мб |
| external-assets-links.txt |
80б |