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种子名称： GetFreeCourses.Co-Udemy-Time Series Analysis, Forecasting, and Machine Learning
文件类型： 视频
文件数目： 119个文件
文件大小： 4.84 GB
收录时间： 2023-1-4 06:43
已经下载： 3次
资源热度： 150
最近下载： 2024-5-10 19:38

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

01 Welcome/001 Introduction and Outline.mp430.69MB

01 Welcome/002 Where to Get the Code.mp461.97MB

01 Welcome/003 Warmup (Optional).mp423.16MB

02 Time Series Basics/001 Time Series Basics Section Introduction.mp417.46MB

02 Time Series Basics/002 What is a Time Series_.mp432.24MB

02 Time Series Basics/003 Modeling vs. Predicting.mp413.48MB

02 Time Series Basics/004 Why Do We Care About Shapes_.mp429.48MB

02 Time Series Basics/005 Types of Tasks.mp423.55MB

02 Time Series Basics/006 Power, Log, and Box-Cox Transformations.mp432.63MB

02 Time Series Basics/007 Power, Log, and Box-Cox Transformations in Code.mp433.29MB

02 Time Series Basics/008 Forecasting Metrics.mp443.71MB

02 Time Series Basics/009 Financial Time Series Primer.mp444.86MB

02 Time Series Basics/010 Price Simulations in Code.mp418.28MB

02 Time Series Basics/011 Random Walks and the Random Walk Hypothesis.mp468.11MB

02 Time Series Basics/012 The Naive Forecast and the Importance of Baselines.mp430.11MB

02 Time Series Basics/013 Naive Forecast and Forecasting Metrics in Code.mp441.47MB

02 Time Series Basics/014 Time Series Basics Section Summary.mp412.13MB

02 Time Series Basics/015 Suggestion Box.mp416.12MB

03 Exponential Smoothing and ETS Methods/001 Exponential Smoothing Section Introduction.mp413.56MB

03 Exponential Smoothing and ETS Methods/002 Exponential Smoothing Intuition for Beginners.mp423.91MB

03 Exponential Smoothing and ETS Methods/003 SMA Theory.mp415.24MB

03 Exponential Smoothing and ETS Methods/004 SMA Code.mp453.57MB

03 Exponential Smoothing and ETS Methods/005 EWMA Theory.mp435.83MB

03 Exponential Smoothing and ETS Methods/006 EWMA Code.mp439.41MB

03 Exponential Smoothing and ETS Methods/007 SES Theory.mp435.57MB

03 Exponential Smoothing and ETS Methods/008 SES Code.mp469.54MB

03 Exponential Smoothing and ETS Methods/009 Holt's Linear Trend Model (Theory).mp433.2MB

03 Exponential Smoothing and ETS Methods/010 Holt's Linear Trend Model (Code).mp419.05MB

03 Exponential Smoothing and ETS Methods/011 Holt-Winters (Theory).mp447.55MB

03 Exponential Smoothing and ETS Methods/012 Holt-Winters (Code).mp449.8MB

03 Exponential Smoothing and ETS Methods/013 Walk-Forward Validation.mp444.31MB

03 Exponential Smoothing and ETS Methods/014 Walk-Forward Validation in Code.mp460.25MB

03 Exponential Smoothing and ETS Methods/015 Application_ Sales Data.mp429.44MB

03 Exponential Smoothing and ETS Methods/016 Application_ Stock Predictions.mp440.51MB

03 Exponential Smoothing and ETS Methods/017 SMA Application_ COVID-19 Counting.mp419.37MB

03 Exponential Smoothing and ETS Methods/018 SMA Application_ Algorithmic Trading.mp411.59MB

03 Exponential Smoothing and ETS Methods/019 Exponential Smoothing Section Summary.mp419.12MB

04 ARIMA/001 ARIMA Section Introduction.mp423.01MB

04 ARIMA/002 Autoregressive Models - AR(p).mp452.54MB

04 ARIMA/003 Moving Average Models - MA(q).mp410.13MB

04 ARIMA/004 ARIMA.mp441.39MB

04 ARIMA/005 ARIMA in Code.mp4121.58MB

04 ARIMA/006 Stationarity.mp455.15MB

04 ARIMA/007 Stationarity in Code.mp461.5MB

04 ARIMA/008 ACF (Autocorrelation Function).mp437MB

04 ARIMA/009 PACF (Partial Autocorrelation Funtion).mp425.11MB

04 ARIMA/010 ACF and PACF in Code (pt 1).mp441.31MB

04 ARIMA/011 ACF and PACF in Code (pt 2).mp433.88MB

04 ARIMA/012 Auto ARIMA and SARIMAX.mp439.45MB

04 ARIMA/013 Model Selection, AIC and BIC.mp445.91MB

04 ARIMA/014 Auto ARIMA in Code.mp4103.19MB

04 ARIMA/015 Auto ARIMA in Code (Stocks).mp4105.21MB

04 ARIMA/016 ACF and PACF for Stock Returns.mp443.5MB

04 ARIMA/017 Auto ARIMA in Code (Sales Data).mp465.42MB

04 ARIMA/018 How to Forecast with ARIMA.mp437.95MB

04 ARIMA/019 ARIMA Section Summary.mp412.74MB

05 Machine Learning Methods/001 Machine Learning Section Introduction.mp417.53MB

05 Machine Learning Methods/002 Supervised Machine Learning_ Classification and Regression.mp468.96MB

05 Machine Learning Methods/003 Autoregressive Machine Learning Models.mp432.38MB

05 Machine Learning Methods/004 Machine Learning Algorithms_ Linear Regression.mp421.8MB

05 Machine Learning Methods/005 Machine Learning Algorithms_ Logistic Regression.mp431.74MB

05 Machine Learning Methods/006 Machine Learning Algorithms_ Support Vector Machines.mp443.52MB

05 Machine Learning Methods/007 Machine Learning Algorithms_ Random Forest.mp432.02MB

05 Machine Learning Methods/008 Extrapolation and Stock Prices.mp464.73MB

05 Machine Learning Methods/009 Machine Learning for Time Series Forecasting in Code (pt 1).mp486.17MB

05 Machine Learning Methods/010 Forecasting with Differencing.mp418.97MB

05 Machine Learning Methods/011 Machine Learning for Time Series Forecasting in Code (pt 2).mp449.4MB

05 Machine Learning Methods/012 Application_ Sales Data.mp442.19MB

05 Machine Learning Methods/013 Application_ Predicting Stock Prices and Returns.mp437.36MB

05 Machine Learning Methods/014 Application_ Predicting Stock Movements.mp426.28MB

05 Machine Learning Methods/015 Machine Learning Section Summary.mp410.36MB

06 Deep Learning_ Artificial Neural Networks (ANN)/001 Artificial Neural Networks_ Section Introduction.mp419.43MB

06 Deep Learning_ Artificial Neural Networks (ANN)/002 The Neuron.mp443.86MB

06 Deep Learning_ Artificial Neural Networks (ANN)/003 Forward Propagation.mp444.79MB

06 Deep Learning_ Artificial Neural Networks (ANN)/004 The Geometrical Picture.mp453.97MB

06 Deep Learning_ Artificial Neural Networks (ANN)/005 Activation Functions.mp486.54MB

06 Deep Learning_ Artificial Neural Networks (ANN)/006 Multiclass Classification.mp443.63MB

06 Deep Learning_ Artificial Neural Networks (ANN)/007 ANN Code Preparation.mp457.51MB

06 Deep Learning_ Artificial Neural Networks (ANN)/008 Feedforward ANN for Time Series Forecasting Code.mp470.91MB

06 Deep Learning_ Artificial Neural Networks (ANN)/009 Feedforward ANN for Stock Return and Price Predictions Code.mp467.71MB

06 Deep Learning_ Artificial Neural Networks (ANN)/010 Human Activity Recognition Dataset.mp430.74MB

06 Deep Learning_ Artificial Neural Networks (ANN)/011 Human Activity Recognition_ Code Preparation.mp431.27MB

06 Deep Learning_ Artificial Neural Networks (ANN)/012 Human Activity Recognition_ Data Exploration.mp449.95MB

06 Deep Learning_ Artificial Neural Networks (ANN)/013 Human Activity Recognition_ Multi-Input ANN.mp467.55MB

06 Deep Learning_ Artificial Neural Networks (ANN)/014 Human Activity Recognition_ Feature-Based Model.mp436.06MB

06 Deep Learning_ Artificial Neural Networks (ANN)/015 Human Activity Recognition_ Combined Model.mp420.9MB

06 Deep Learning_ Artificial Neural Networks (ANN)/016 How Does a Neural Network _Learn__.mp450.07MB

06 Deep Learning_ Artificial Neural Networks (ANN)/017 Artificial Neural Networks_ Section Summary.mp410.95MB

07 Deep Learning_ Convolutional Neural Networks (CNN)/001 CNN Section Introduction.mp414.31MB

07 Deep Learning_ Convolutional Neural Networks (CNN)/002 What is Convolution_.mp478.29MB

07 Deep Learning_ Convolutional Neural Networks (CNN)/003 What is Convolution_ (Pattern-Matching).mp423.69MB

07 Deep Learning_ Convolutional Neural Networks (CNN)/004 What is Convolution_ (Weight Sharing).mp430.44MB

07 Deep Learning_ Convolutional Neural Networks (CNN)/005 Convolution on Color Images.mp473.99MB

07 Deep Learning_ Convolutional Neural Networks (CNN)/006 Convolution for Time Series and ARIMA.mp423.61MB

07 Deep Learning_ Convolutional Neural Networks (CNN)/007 CNN Architecture.mp496.82MB

07 Deep Learning_ Convolutional Neural Networks (CNN)/008 CNN Code Preparation.mp427.49MB

07 Deep Learning_ Convolutional Neural Networks (CNN)/009 CNN for Time Series Forecasting in Code.mp448.78MB

07 Deep Learning_ Convolutional Neural Networks (CNN)/010 CNN for Human Activity Recognition.mp446.39MB

07 Deep Learning_ Convolutional Neural Networks (CNN)/011 CNN Section Summary.mp415.43MB

08 VIP_ AWS Forecast/001 AWS Forecast Section Introduction.mp443.54MB

08 VIP_ AWS Forecast/002 Data Model.mp448.96MB

08 VIP_ AWS Forecast/003 Creating an IAM Role.mp423.8MB

08 VIP_ AWS Forecast/004 Code pt 1 (Getting and Transforming the Data).mp463.34MB

08 VIP_ AWS Forecast/005 Code pt 2 (Uploading the data to S3).mp491.06MB

08 VIP_ AWS Forecast/006 Code pt 3 (Building your Model).mp454.47MB

08 VIP_ AWS Forecast/007 Code pt 4 (Generating and Evaluating the Forecast).mp449.88MB

08 VIP_ AWS Forecast/008 AWS Forecast Exercise.mp413.76MB

08 VIP_ AWS Forecast/009 AWS Forecast Section Summary.mp425.46MB

10 Setting Up Your Environment FAQ/001 Anaconda Environment Setup.mp427.88MB

10 Setting Up Your Environment FAQ/002 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp443.61MB

11 Extra Help With Python Coding for Beginners FAQ/001 How to Code by Yourself (part 1).mp424.59MB

11 Extra Help With Python Coding for Beginners FAQ/002 How to Code by Yourself (part 2).mp449.18MB

11 Extra Help With Python Coding for Beginners FAQ/003 Proof that using Jupyter Notebook is the same as not using it.mp469.51MB

12 Effective Learning Strategies for Machine Learning FAQ/001 How to Succeed in this Course (Long Version).mp412.6MB

12 Effective Learning Strategies for Machine Learning FAQ/002 Is this for Beginners or Experts_ Academic or Practical_ Fast or slow-paced_.mp438.95MB

12 Effective Learning Strategies for Machine Learning FAQ/003 Machine Learning and AI Prerequisite Roadmap (pt 1).mp479.62MB

12 Effective Learning Strategies for Machine Learning FAQ/004 Machine Learning and AI Prerequisite Roadmap (pt 2).mp4108.19MB

13 Appendix _ FAQ Finale/001 What is the Appendix_.mp416.4MB

13 Appendix _ FAQ Finale/002 BONUS_ Where to get discount coupons and FREE deep learning material.mp437.81MB