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种子名称： [Tutorialsplanet.NET] Udemy - 2019 AWS SageMaker and Machine Learning - With Python
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文件数目： 171个文件
文件大小： 4.59 GB
收录时间： 2020-8-7 05:08
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最近下载： 2025-1-9 10:43

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[Tutorialsplanet.NET] Udemy - 2019 AWS SageMaker and Machine Learning - With Python.torrent

1. Introduction and Housekeeping/1. Introduction.mp43.62MB

1. Introduction and Housekeeping/2. Root Account Setup and Billing Dashboard Overview.mp45.96MB

1. Introduction and Housekeeping/3. Enable Access to Billing Data for IAM Users.mp49.71MB

1. Introduction and Housekeeping/4. Create Users Required For the Course.mp425.8MB

1. Introduction and Housekeeping/5. AWS Command Line Interface Tool Setup and Summary.mp47.21MB

1. Introduction and Housekeeping/6. Six Advantages of Cloud Computing.mp430.31MB

1. Introduction and Housekeeping/7. AWS Global Infrastructure Overview.mp440.08MB

10. 2019 SageMaker HyperParameter Tuning/2. Introduction to Hyperparameter Tuning.mp442.37MB

10. 2019 SageMaker HyperParameter Tuning/3. Lab Tuning Movie Rating Factorization Machine Recommender System.mp4154.02MB

10. 2019 SageMaker HyperParameter Tuning/4. Lab Step 2 Tuning Movie Rating Recommender System.mp448.13MB

11. AWS Machine Learning Service/10. Data Types supported by AWS Machine Learning.mp45.34MB

11. AWS Machine Learning Service/11. Linear Regression Introduction.mp412.67MB

11. AWS Machine Learning Service/12. Binary Classification Introduction.mp49.19MB

11. AWS Machine Learning Service/13. Multiclass Classification Introduction.mp46.03MB

11. AWS Machine Learning Service/14. Data Visualization - Linear, Log, Quadratic and More.mp417.42MB

11. AWS Machine Learning Service/2. Python Development Environment and Boto3 Setup.mp414.96MB

11. AWS Machine Learning Service/3. Project Source Code and Data Setup.mp410MB

11. AWS Machine Learning Service/4. Lab Intro to Python Jupyter Notebook Environment, Pandas, Matplotlib.mp431.75MB

11. AWS Machine Learning Service/5. Lab AWS S3 Bucket Setup and Configure Security.mp418.06MB

11. AWS Machine Learning Service/6. Summary.mp42.17MB

11. AWS Machine Learning Service/9. Machine Learning Terminology.mp47.05MB

12. Linear Regression/1. Lab Linear Model, Squared Error Loss Function, Stochastic Gradient Descent.mp431.26MB

12. Linear Regression/2. Lab Linear Regression for complex shapes.mp411.43MB

12. Linear Regression/3. Summary.mp43.86MB

13. AWS - Linear Regression Models/1. Lab Simple Training Data.mp415.48MB

13. AWS - Linear Regression Models/2. Lab Datasource.mp428.93MB

13. AWS - Linear Regression Models/3. Lab Train Model with default recipe.mp49.8MB

13. AWS - Linear Regression Models/5. Concept - How to evaluate regression model accuracy.mp49.53MB

13. AWS - Linear Regression Models/6. Lab Evaluate predictive quality of the trained model.mp428.72MB

13. AWS - Linear Regression Models/7. Lab Review Default Recipe Settings Used To Train model.mp44.56MB

13. AWS - Linear Regression Models/8. Lab Train Model With Custom Recipe and Review Performance.mp422.05MB

13. AWS - Linear Regression Models/9. Model Performance Summary and Conclusion.mp45.06MB

14. Adding Features To Improve Model/1. Lab Quadratic Fit Training Data.mp415.41MB

14. Adding Features To Improve Model/2. Lab Underfitting With Linear Features.mp444.89MB

14. Adding Features To Improve Model/3. Lab Normal Fit With Quadratic Features.mp427.3MB

14. Adding Features To Improve Model/4. Summary.mp43.28MB

15. Normalization/1. Lab Impact of Features With Different Magnitude.mp437.69MB

15. Normalization/2. Concept Normalization to smoothen magnitude differences.mp413.24MB

15. Normalization/3. Lab Train Model With Feature Normalizaton.mp423.01MB

15. Normalization/4. Summary.mp43.17MB

16. Adding Complex Features/1. Lab Prepare Training Data.mp48.14MB

16. Adding Complex Features/2. Lab Adding Complex Features.mp44.8MB

16. Adding Complex Features/3. Lab Train Model With Higher Order Features.mp426.54MB

16. Adding Complex Features/4. Lab Performance Of Model With Degree 1 Features.mp46.98MB

16. Adding Complex Features/5. Lab Performance of Model with Degree 4 Features.mp46.62MB

16. Adding Complex Features/6. Lab Performance of Model With Degree 15 Features.mp43.68MB

16. Adding Complex Features/7. Summary.mp43.66MB

17. Kaggle Bike Hourly Rental Prediction/1. Review Kaggle Bike Train Problem And Dataset.mp437.86MB

17. Kaggle Bike Hourly Rental Prediction/2. Lab Train Model To Predict Hourly Rental.mp413.32MB

17. Kaggle Bike Hourly Rental Prediction/3. Lab Evaluate Prediction Quality.mp423.09MB

17. Kaggle Bike Hourly Rental Prediction/4. Linear Regression Wrapup and Summary.mp43.39MB

18. Logistic Regression/1. Binary Classification - Logistic Regression, Loss Function, Optimization.mp419.68MB

18. Logistic Regression/2. Lab Binary Classification Approach.mp419.63MB

18. Logistic Regression/3. True Positive, True Negative, False Positive and False Negative.mp418.68MB

18. Logistic Regression/4. Lab Logistic Optimization Objectives.mp412.58MB

18. Logistic Regression/5. Lab Logistic Cost Function.mp47.46MB

18. Logistic Regression/6. Lab Cost Example.mp49.14MB

18. Logistic Regression/7. Optimizing Weights.mp49.21MB

18. Logistic Regression/8. Summary.mp47.04MB

19. Onset of Diabetes Prediction/1. Problem Objective, Input Data and Strategy.mp422.37MB

19. Onset of Diabetes Prediction/10. Lab Batch Prediction and Compute Metrics.mp422.66MB

19. Onset of Diabetes Prediction/11. Summary.mp44.39MB

19. Onset of Diabetes Prediction/2. Lab Prepare For Training.mp48.59MB

19. Onset of Diabetes Prediction/3. Lab Training a Classification Model.mp413.23MB

19. Onset of Diabetes Prediction/4. Concept Classification Metrics.mp410.29MB

19. Onset of Diabetes Prediction/5. Concept Classification Insights with AWS Histograms.mp412.62MB

19. Onset of Diabetes Prediction/6. Concept AUC Metric.mp44.17MB

19. Onset of Diabetes Prediction/7. Lab Review Diabetes Model Performance.mp418.01MB

19. Onset of Diabetes Prediction/8. Lab Cutoff Threshold Interactive Testing.mp46.21MB

19. Onset of Diabetes Prediction/9. Lab Evaluating Prediction Quality With Additional Dataset.mp419.94MB

2. 2019 SageMaker Housekeeping/2. Demo - S3 Bucket Setup.mp420.59MB

2. 2019 SageMaker Housekeeping/3. Demo - Setup SageMaker Notebook Instance.mp441.93MB

2. 2019 SageMaker Housekeeping/4. 2019 Demo - Source Code and Data Setup.mp433.32MB

20. Multiclass Classifiers using Multinomial Logistic Regression/1. Lab Iris Classifcation.mp421.1MB

20. Multiclass Classifiers using Multinomial Logistic Regression/2. Lab Train Classifier with Default and Custom Recipe.mp423.61MB

20. Multiclass Classifiers using Multinomial Logistic Regression/3. Concept Evaluating Predictive Quality of Multiclass Classifiers.mp45MB

20. Multiclass Classifiers using Multinomial Logistic Regression/4. Concept Confusion Matrix To Evaluating Predictive Quality.mp49.95MB

20. Multiclass Classifiers using Multinomial Logistic Regression/5. Lab Evaluate Performance of Iris Classifiers using Default Recipe.mp413.36MB

20. Multiclass Classifiers using Multinomial Logistic Regression/6. Lab Evaluate Performance of Iris Classifiers using Custom Recipe.mp49.95MB

20. Multiclass Classifiers using Multinomial Logistic Regression/7. Lab Batch Prediction and Computing Metrics using Python Code.mp426.95MB

20. Multiclass Classifiers using Multinomial Logistic Regression/8. Summary.mp46.58MB

21. Text Based Classification with AWS Twitter Dataset/1. AWS Twitter Feed Classification for Customer Service.mp414.57MB

21. Text Based Classification with AWS Twitter Dataset/2. Lab Train, Evaluate Model and Assess Predictive Quality.mp429.03MB

21. Text Based Classification with AWS Twitter Dataset/3. Lab Interactive Prediction with AWS.mp411.74MB

21. Text Based Classification with AWS Twitter Dataset/4. Logistic Regression Summary.mp41.52MB

22. Data Transformation using Recipes/1. Recipe Overview.mp48.49MB

22. Data Transformation using Recipes/2. Recipe Example.mp410.18MB

22. Data Transformation using Recipes/3. Text Transformation.mp413.27MB

22. Data Transformation using Recipes/4. Numeric Transformation - Quantile Binning.mp44.63MB

22. Data Transformation using Recipes/5. Numeric Transformation - Normalization.mp46.99MB

22. Data Transformation using Recipes/6. Cartesian Product Transformation - Categorical and Text.mp43.95MB

22. Data Transformation using Recipes/7. Summary.mp4711.87KB

23. Hyper Parameters, Model Optimization and Lifecycle/1. Introduction.mp41.02MB

23. Hyper Parameters, Model Optimization and Lifecycle/2. Data Rearrangement, Maximum Model Size, Passes, Shuffle Type.mp415.34MB

23. Hyper Parameters, Model Optimization and Lifecycle/3. Regularization, Learning Rate.mp45.76MB

23. Hyper Parameters, Model Optimization and Lifecycle/4. Regularization Effect.mp45.94MB

23. Hyper Parameters, Model Optimization and Lifecycle/5. Improving Model Quality.mp414.1MB

23. Hyper Parameters, Model Optimization and Lifecycle/6. Model Maintenance.mp413.22MB

23. Hyper Parameters, Model Optimization and Lifecycle/7. AWS Machine Learning System Limits.mp44.32MB

23. Hyper Parameters, Model Optimization and Lifecycle/8. AWS Machine Learning Pricing.mp44.94MB

24. Integration of AWS Machine Learning With Your Application/1. Introduction.mp45.32MB

24. Integration of AWS Machine Learning With Your Application/10. Demo Allowing Prediction Only For Registered Users.mp43.51MB

24. Integration of AWS Machine Learning With Your Application/11. Cognito Overview.mp43.61MB

24. Integration of AWS Machine Learning With Your Application/12. Lab Cognito User Pool Configuration.mp419.63MB

24. Integration of AWS Machine Learning With Your Application/13. Lab AngularJS Web Client - Invoke Prediction for authorized users.mp441.97MB

24. Integration of AWS Machine Learning With Your Application/14. Lab Invoke Machine Learning Service From AWS EC2 Instance.mp416.03MB

24. Integration of AWS Machine Learning With Your Application/15. Summary.mp4884.55KB

24. Integration of AWS Machine Learning With Your Application/2. Integration Scenarios.mp44.56MB

24. Integration of AWS Machine Learning With Your Application/3. Security using IAM.mp47.3MB

24. Integration of AWS Machine Learning With Your Application/4. Hands-on lab - List of Demos and Objective.mp44.88MB

24. Integration of AWS Machine Learning With Your Application/5. Lab Enable Real Time End Point and Configure IAM Prediction User.mp418.83MB

24. Integration of AWS Machine Learning With Your Application/6. Lab Invoking Prediction From AWS Command Line Interface.mp415.09MB

24. Integration of AWS Machine Learning With Your Application/7. Lab Invoking Prediction From Python Client.mp410.5MB

24. Integration of AWS Machine Learning With Your Application/8. Lab Python Client to Train, Evaluate Models and Integrate with AWS.mp437.41MB

24. Integration of AWS Machine Learning With Your Application/9. Lab Invoking Prediction From Web Page AngularJS Client.mp420.39MB

26. Conclusion/2. Conclusion.mp41.27MB

3. 2019 Machine Learning Concepts/1. 2019 Introduction to Machine Learning, Concepts, Terminologies.mp470.19MB

3. 2019 Machine Learning Concepts/2. 2019 Data Types - How to handle mixed data types.mp4102.2MB

3. 2019 Machine Learning Concepts/3. 2019 Introduction to Python Notebook Environment.mp485.57MB

3. 2019 Machine Learning Concepts/4. 2019 Introduction to working with Missing Data.mp481.7MB

3. 2019 Machine Learning Concepts/5. 2019 Data Visualization - Linear, Log, Quadratic and More.mp437.8MB

4. 2019 SageMaker Service Overview/2. SageMaker Overview.mp413.81MB

4. 2019 SageMaker Service Overview/3. Compute Instance Families and Pricing.mp419.82MB

4. 2019 SageMaker Service Overview/4. Algorithms and Data Formats Supported For Training and Inference.mp49.58MB

5. XGBoost - Gradient Boosted Trees/1. Introduction to XGBoost.mp472.6MB

5. XGBoost - Gradient Boosted Trees/10. Demo - Training on SageMaker Cloud - Kaggle Bike Rental Model Version 3.mp4127.23MB

5. XGBoost - Gradient Boosted Trees/11. Demo - Invoking SageMaker Model Endpoints For Real Time Predictions.mp445.34MB

5. XGBoost - Gradient Boosted Trees/12. Demo - Invoking SageMaker Model Endpoints From Client Outside of AWS.mp427.67MB

5. XGBoost - Gradient Boosted Trees/15. XGBoost Hyper Parameter Tuning.mp451.22MB

5. XGBoost - Gradient Boosted Trees/16. Demo - XGBoost Multi-Class Classification Iris Data.mp481.81MB

5. XGBoost - Gradient Boosted Trees/17. Demo - XGBoost Binary Classifier For Diabetes Prediction.mp445.25MB

5. XGBoost - Gradient Boosted Trees/18. Demo - XGBoost Binary Classifier for Edible Mushroom Prediction.mp447.36MB

5. XGBoost - Gradient Boosted Trees/19. Summary - XGBoost.mp413.23MB

5. XGBoost - Gradient Boosted Trees/2. Source Code Overview.mp417.12MB

5. XGBoost - Gradient Boosted Trees/3. Demo - Create Files in SageMaker Data Formats and Save Files To S3.mp463.1MB

5. XGBoost - Gradient Boosted Trees/4. Demo - Working with XGBoost - Linear Regression Straight Line Fit.mp499.68MB

5. XGBoost - Gradient Boosted Trees/5. Demo - XGBoost Example with Quadratic Fit.mp434.8MB

5. XGBoost - Gradient Boosted Trees/6. Demo - Kaggle Bike Rental Data Setup, Exploration and Preparation.mp497.12MB

5. XGBoost - Gradient Boosted Trees/7. Demo - Kaggle Bike Rental Model Version 1.mp496.16MB

5. XGBoost - Gradient Boosted Trees/8. Demo - Kaggle Bike Rental Model Version 2.mp441.91MB

5. XGBoost - Gradient Boosted Trees/9. Demo - Kaggle Bike Rental Model Version 3.mp435.5MB

6. SageMaker - Principal Component Analysis (PCA)/10. Demo - PCA Projection with SageMaker.mp424.31MB

6. SageMaker - Principal Component Analysis (PCA)/12. Summary.mp46.7MB

6. SageMaker - Principal Component Analysis (PCA)/2. Introduction to Principal Component Analysis (PCA).mp452.59MB

6. SageMaker - Principal Component Analysis (PCA)/3. PCA Demo Overview.mp45.1MB

6. SageMaker - Principal Component Analysis (PCA)/4. Demo - PCA with Random Dataset.mp426.64MB

6. SageMaker - Principal Component Analysis (PCA)/5. Demo - PCA with Correlated Dataset.mp447.2MB

6. SageMaker - Principal Component Analysis (PCA)/7. Demo - PCA with Kaggle Bike Sharing - Overview and Normalization.mp432.79MB

6. SageMaker - Principal Component Analysis (PCA)/8. Demo - PCA Local Model with Kaggle Bike Train.mp430.48MB

6. SageMaker - Principal Component Analysis (PCA)/9. Demo - PCA training with SageMaker.mp438.75MB

7. SageMaker - Factorization Machines/2. Introduction to Factorization Machines.mp436.09MB

7. SageMaker - Factorization Machines/4. Demo - Movie Recommender Data Preparation.mp490.7MB

7. SageMaker - Factorization Machines/5. Demo - Movie Recommender Model Training.mp449.05MB

7. SageMaker - Factorization Machines/6. Demo - Movie Predictions By User.mp468.99MB

8. SageMaker - DeepAR Time Series Forecasting/10. Demo - DeepAR Dynamic Features Training and Prediction.mp426.85MB

8. SageMaker - DeepAR Time Series Forecasting/11. Summary.mp410.97MB

8. SageMaker - DeepAR Time Series Forecasting/2. Introduction to DeepAR Time Series Forecasting.mp475.8MB

8. SageMaker - DeepAR Time Series Forecasting/3. DeepAR Training and Inference Formats.mp489.42MB

8. SageMaker - DeepAR Time Series Forecasting/4. Working with Time Series Data, Handling Missing Values.mp465.93MB

8. SageMaker - DeepAR Time Series Forecasting/5. Demo - Bike Rental as Time Series Forecasting Problem.mp4104.99MB

8. SageMaker - DeepAR Time Series Forecasting/6. Demo - Bike Rental Model Training.mp477.28MB

8. SageMaker - DeepAR Time Series Forecasting/7. Demo - Bike Rental Prediction.mp448.62MB

8. SageMaker - DeepAR Time Series Forecasting/8. Demo - DeepAR Categories.mp464.56MB

8. SageMaker - DeepAR Time Series Forecasting/9. Demo - DeepAR Dynamic Features Data Preparation.mp467.57MB

9. 2019 Integration Options - Model Endpoint/2. Integration Overview.mp411.75MB

9. 2019 Integration Options - Model Endpoint/3. Install Python and Boto3 - Local Machine.mp413.65MB

9. 2019 Integration Options - Model Endpoint/5. Client to Endpoint using SageMaker SDK.mp476.98MB

9. 2019 Integration Options - Model Endpoint/6. Client to Endpoint using Boto3 SDK.mp438.28MB

9. 2019 Integration Options - Model Endpoint/7. Microservice - Lambda to Endpoint - Payload.mp423.68MB

9. 2019 Integration Options - Model Endpoint/8. Microservice - Lambda to Endpoint.mp474.18MB

9. 2019 Integration Options - Model Endpoint/9. Microservice - API Gateway, Lambda to Endpoint.mp484.07MB