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

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

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文件列表

  • 10. 2019 SageMaker HyperParameter Tuning/3. Lab Tuning Movie Rating Factorization Machine Recommender System.mp4 161.5 MB
  • 5. XGBoost - Gradient Boosted Trees/10. Demo - Training on SageMaker Cloud - Kaggle Bike Rental Model Version 3.mp4 133.4 MB
  • 8. SageMaker - DeepAR Time Series Forecasting/5. Demo - Bike Rental as Time Series Forecasting Problem.mp4 110.1 MB
  • 3. 2019 Machine Learning Concepts/2. 2019 Data Types - How to handle mixed data types.mp4 107.2 MB
  • 5. XGBoost - Gradient Boosted Trees/4. Demo - Working with XGBoost - Linear Regression Straight Line Fit.mp4 104.5 MB
  • 5. XGBoost - Gradient Boosted Trees/6. Demo - Kaggle Bike Rental Data Setup, Exploration and Preparation.mp4 101.8 MB
  • 5. XGBoost - Gradient Boosted Trees/7. Demo - Kaggle Bike Rental Model Version 1.mp4 100.8 MB
  • 7. SageMaker - Factorization Machines/4. Demo - Movie Recommender Data Preparation.mp4 95.1 MB
  • 8. SageMaker - DeepAR Time Series Forecasting/3. DeepAR Training and Inference Formats.mp4 93.8 MB
  • 3. 2019 Machine Learning Concepts/3. 2019 Introduction to Python Notebook Environment.mp4 89.7 MB
  • 9. 2019 Integration Options - Model Endpoint/9. Microservice - API Gateway, Lambda to Endpoint.mp4 88.2 MB
  • 5. XGBoost - Gradient Boosted Trees/16. Demo - XGBoost Multi-Class Classification Iris Data.mp4 85.8 MB
  • 3. 2019 Machine Learning Concepts/4. 2019 Introduction to working with Missing Data.mp4 85.7 MB
  • 8. SageMaker - DeepAR Time Series Forecasting/6. Demo - Bike Rental Model Training.mp4 81.0 MB
  • 9. 2019 Integration Options - Model Endpoint/5. Client to Endpoint using SageMaker SDK.mp4 80.7 MB
  • 8. SageMaker - DeepAR Time Series Forecasting/2. Introduction to DeepAR Time Series Forecasting.mp4 79.5 MB
  • 9. 2019 Integration Options - Model Endpoint/8. Microservice - Lambda to Endpoint.mp4 77.8 MB
  • 5. XGBoost - Gradient Boosted Trees/1. Introduction to XGBoost.mp4 76.1 MB
  • 3. 2019 Machine Learning Concepts/1. 2019 Introduction to Machine Learning, Concepts, Terminologies.mp4 73.6 MB
  • 7. SageMaker - Factorization Machines/6. Demo - Movie Predictions By User.mp4 72.3 MB
  • 8. SageMaker - DeepAR Time Series Forecasting/9. Demo - DeepAR Dynamic Features Data Preparation.mp4 70.9 MB
  • 8. SageMaker - DeepAR Time Series Forecasting/4. Working with Time Series Data, Handling Missing Values.mp4 69.1 MB
  • 8. SageMaker - DeepAR Time Series Forecasting/8. Demo - DeepAR Categories.mp4 67.7 MB
  • 5. XGBoost - Gradient Boosted Trees/3. Demo - Create Files in SageMaker Data Formats and Save Files To S3.mp4 66.2 MB
  • 6. SageMaker - Principal Component Analysis (PCA)/2. Introduction to Principal Component Analysis (PCA).mp4 55.1 MB
  • 5. XGBoost - Gradient Boosted Trees/15. XGBoost Hyper Parameter Tuning.mp4 53.7 MB
  • 7. SageMaker - Factorization Machines/5. Demo - Movie Recommender Model Training.mp4 51.4 MB
  • 8. SageMaker - DeepAR Time Series Forecasting/7. Demo - Bike Rental Prediction.mp4 51.0 MB
  • 10. 2019 SageMaker HyperParameter Tuning/4. Lab Step 2 Tuning Movie Rating Recommender System.mp4 50.5 MB
  • 5. XGBoost - Gradient Boosted Trees/18. Demo - XGBoost Binary Classifier for Edible Mushroom Prediction.mp4 49.7 MB
  • 6. SageMaker - Principal Component Analysis (PCA)/5. Demo - PCA with Correlated Dataset.mp4 49.5 MB
  • 5. XGBoost - Gradient Boosted Trees/11. Demo - Invoking SageMaker Model Endpoints For Real Time Predictions.mp4 47.5 MB
  • 5. XGBoost - Gradient Boosted Trees/17. Demo - XGBoost Binary Classifier For Diabetes Prediction.mp4 47.4 MB
  • 14. Adding Features To Improve Model/2. Lab Underfitting With Linear Features.mp4 47.1 MB
  • 10. 2019 SageMaker HyperParameter Tuning/2. Introduction to Hyperparameter Tuning.mp4 44.4 MB
  • 24. Integration of AWS Machine Learning With Your Application/13. Lab AngularJS Web Client - Invoke Prediction for authorized users.mp4 44.0 MB
  • 2. 2019 SageMaker Housekeeping/3. Demo - Setup SageMaker Notebook Instance.mp4 44.0 MB
  • 5. XGBoost - Gradient Boosted Trees/8. Demo - Kaggle Bike Rental Model Version 2.mp4 43.9 MB
  • 1. Introduction and Housekeeping/7. AWS Global Infrastructure Overview.mp4 42.0 MB
  • 6. SageMaker - Principal Component Analysis (PCA)/9. Demo - PCA training with SageMaker.mp4 40.6 MB
  • 9. 2019 Integration Options - Model Endpoint/6. Client to Endpoint using Boto3 SDK.mp4 40.1 MB
  • 17. Kaggle Bike Hourly Rental Prediction/1. Review Kaggle Bike Train Problem And Dataset.mp4 39.7 MB
  • 3. 2019 Machine Learning Concepts/5. 2019 Data Visualization - Linear, Log, Quadratic and More.mp4 39.6 MB
  • 15. Normalization/1. Lab Impact of Features With Different Magnitude.mp4 39.5 MB
  • 24. Integration of AWS Machine Learning With Your Application/8. Lab Python Client to Train, Evaluate Models and Integrate with AWS.mp4 39.2 MB
  • 7. SageMaker - Factorization Machines/2. Introduction to Factorization Machines.mp4 37.8 MB
  • 5. XGBoost - Gradient Boosted Trees/9. Demo - Kaggle Bike Rental Model Version 3.mp4 37.2 MB
  • 5. XGBoost - Gradient Boosted Trees/5. Demo - XGBoost Example with Quadratic Fit.mp4 36.5 MB
  • 2. 2019 SageMaker Housekeeping/4. 2019 Demo - Source Code and Data Setup.mp4 34.9 MB
  • 6. SageMaker - Principal Component Analysis (PCA)/7. Demo - PCA with Kaggle Bike Sharing - Overview and Normalization.mp4 34.4 MB
  • 11. AWS Machine Learning Service/4. Lab Intro to Python Jupyter Notebook Environment, Pandas, Matplotlib.mp4 33.3 MB
  • 12. Linear Regression/1. Lab Linear Model, Squared Error Loss Function, Stochastic Gradient Descent.mp4 32.8 MB
  • 6. SageMaker - Principal Component Analysis (PCA)/8. Demo - PCA Local Model with Kaggle Bike Train.mp4 32.0 MB
  • 1. Introduction and Housekeeping/6. Six Advantages of Cloud Computing.mp4 31.8 MB
  • 21. Text Based Classification with AWS Twitter Dataset/2. Lab Train, Evaluate Model and Assess Predictive Quality.mp4 30.4 MB
  • 13. AWS - Linear Regression Models/2. Lab Datasource.mp4 30.3 MB
  • 13. AWS - Linear Regression Models/6. Lab Evaluate predictive quality of the trained model.mp4 30.1 MB
  • 5. XGBoost - Gradient Boosted Trees/12. Demo - Invoking SageMaker Model Endpoints From Client Outside of AWS.mp4 29.0 MB
  • 14. Adding Features To Improve Model/3. Lab Normal Fit With Quadratic Features.mp4 28.6 MB
  • 20. Multiclass Classifiers using Multinomial Logistic Regression/7. Lab Batch Prediction and Computing Metrics using Python Code.mp4 28.3 MB
  • 8. SageMaker - DeepAR Time Series Forecasting/10. Demo - DeepAR Dynamic Features Training and Prediction.mp4 28.2 MB
  • 6. SageMaker - Principal Component Analysis (PCA)/4. Demo - PCA with Random Dataset.mp4 27.9 MB
  • 16. Adding Complex Features/3. Lab Train Model With Higher Order Features.mp4 27.8 MB
  • 1. Introduction and Housekeeping/4. Create Users Required For the Course.mp4 27.1 MB
  • 6. SageMaker - Principal Component Analysis (PCA)/10. Demo - PCA Projection with SageMaker.mp4 25.5 MB
  • 9. 2019 Integration Options - Model Endpoint/7. Microservice - Lambda to Endpoint - Payload.mp4 24.8 MB
  • 20. Multiclass Classifiers using Multinomial Logistic Regression/2. Lab Train Classifier with Default and Custom Recipe.mp4 24.8 MB
  • 17. Kaggle Bike Hourly Rental Prediction/3. Lab Evaluate Prediction Quality.mp4 24.2 MB
  • 15. Normalization/3. Lab Train Model With Feature Normalizaton.mp4 24.1 MB
  • 19. Onset of Diabetes Prediction/10. Lab Batch Prediction and Compute Metrics.mp4 23.8 MB
  • 19. Onset of Diabetes Prediction/1. Problem Objective, Input Data and Strategy.mp4 23.5 MB
  • 13. AWS - Linear Regression Models/8. Lab Train Model With Custom Recipe and Review Performance.mp4 23.1 MB
  • 20. Multiclass Classifiers using Multinomial Logistic Regression/1. Lab Iris Classifcation.mp4 22.1 MB
  • 2. 2019 SageMaker Housekeeping/2. Demo - S3 Bucket Setup.mp4 21.6 MB
  • 24. Integration of AWS Machine Learning With Your Application/9. Lab Invoking Prediction From Web Page AngularJS Client.mp4 21.4 MB
  • 19. Onset of Diabetes Prediction/9. Lab Evaluating Prediction Quality With Additional Dataset.mp4 20.9 MB
  • 4. 2019 SageMaker Service Overview/3. Compute Instance Families and Pricing.mp4 20.8 MB
  • 18. Logistic Regression/1. Binary Classification - Logistic Regression, Loss Function, Optimization.mp4 20.6 MB
  • 24. Integration of AWS Machine Learning With Your Application/12. Lab Cognito User Pool Configuration.mp4 20.6 MB
  • 18. Logistic Regression/2. Lab Binary Classification Approach.mp4 20.6 MB
  • 24. Integration of AWS Machine Learning With Your Application/5. Lab Enable Real Time End Point and Configure IAM Prediction User.mp4 19.7 MB
  • 18. Logistic Regression/3. True Positive, True Negative, False Positive and False Negative.mp4 19.6 MB
  • 11. AWS Machine Learning Service/5. Lab AWS S3 Bucket Setup and Configure Security.mp4 18.9 MB
  • 19. Onset of Diabetes Prediction/7. Lab Review Diabetes Model Performance.mp4 18.9 MB
  • 11. AWS Machine Learning Service/14. Data Visualization - Linear, Log, Quadratic and More.mp4 18.3 MB
  • 5. XGBoost - Gradient Boosted Trees/2. Source Code Overview.mp4 18.0 MB
  • 24. Integration of AWS Machine Learning With Your Application/14. Lab Invoke Machine Learning Service From AWS EC2 Instance.mp4 16.8 MB
  • 13. AWS - Linear Regression Models/1. Lab Simple Training Data.mp4 16.2 MB
  • 14. Adding Features To Improve Model/1. Lab Quadratic Fit Training Data.mp4 16.2 MB
  • 23. Hyper Parameters, Model Optimization and Lifecycle/2. Data Rearrangement, Maximum Model Size, Passes, Shuffle Type.mp4 16.1 MB
  • 24. Integration of AWS Machine Learning With Your Application/6. Lab Invoking Prediction From AWS Command Line Interface.mp4 15.8 MB
  • 11. AWS Machine Learning Service/2. Python Development Environment and Boto3 Setup.mp4 15.7 MB
  • 21. Text Based Classification with AWS Twitter Dataset/1. AWS Twitter Feed Classification for Customer Service.mp4 15.3 MB
  • 23. Hyper Parameters, Model Optimization and Lifecycle/5. Improving Model Quality.mp4 14.8 MB
  • 4. 2019 SageMaker Service Overview/2. SageMaker Overview.mp4 14.5 MB
  • 9. 2019 Integration Options - Model Endpoint/3. Install Python and Boto3 - Local Machine.mp4 14.3 MB
  • 20. Multiclass Classifiers using Multinomial Logistic Regression/5. Lab Evaluate Performance of Iris Classifiers using Default Recipe.mp4 14.0 MB
  • 17. Kaggle Bike Hourly Rental Prediction/2. Lab Train Model To Predict Hourly Rental.mp4 14.0 MB
  • 22. Data Transformation using Recipes/3. Text Transformation.mp4 13.9 MB
  • 15. Normalization/2. Concept Normalization to smoothen magnitude differences.mp4 13.9 MB
  • 5. XGBoost - Gradient Boosted Trees/19. Summary - XGBoost.mp4 13.9 MB
  • 19. Onset of Diabetes Prediction/3. Lab Training a Classification Model.mp4 13.9 MB
  • 23. Hyper Parameters, Model Optimization and Lifecycle/6. Model Maintenance.mp4 13.9 MB
  • 11. AWS Machine Learning Service/11. Linear Regression Introduction.mp4 13.3 MB
  • 19. Onset of Diabetes Prediction/5. Concept Classification Insights with AWS Histograms.mp4 13.2 MB
  • 18. Logistic Regression/4. Lab Logistic Optimization Objectives.mp4 13.2 MB
  • 9. 2019 Integration Options - Model Endpoint/2. Integration Overview.mp4 12.3 MB
  • 21. Text Based Classification with AWS Twitter Dataset/3. Lab Interactive Prediction with AWS.mp4 12.3 MB
  • 12. Linear Regression/2. Lab Linear Regression for complex shapes.mp4 12.0 MB
  • 8. SageMaker - DeepAR Time Series Forecasting/11. Summary.mp4 11.5 MB
  • 24. Integration of AWS Machine Learning With Your Application/7. Lab Invoking Prediction From Python Client.mp4 11.0 MB
  • 19. Onset of Diabetes Prediction/4. Concept Classification Metrics.mp4 10.8 MB
  • 22. Data Transformation using Recipes/2. Recipe Example.mp4 10.7 MB
  • 11. AWS Machine Learning Service/3. Project Source Code and Data Setup.mp4 10.5 MB
  • 20. Multiclass Classifiers using Multinomial Logistic Regression/4. Concept Confusion Matrix To Evaluating Predictive Quality.mp4 10.4 MB
  • 20. Multiclass Classifiers using Multinomial Logistic Regression/6. Lab Evaluate Performance of Iris Classifiers using Custom Recipe.mp4 10.4 MB
  • 13. AWS - Linear Regression Models/3. Lab Train Model with default recipe.mp4 10.3 MB
  • 1. Introduction and Housekeeping/3. Enable Access to Billing Data for IAM Users.mp4 10.2 MB
  • 4. 2019 SageMaker Service Overview/4. Algorithms and Data Formats Supported For Training and Inference.mp4 10.0 MB
  • 13. AWS - Linear Regression Models/5. Concept - How to evaluate regression model accuracy.mp4 10.0 MB
  • 18. Logistic Regression/7. Optimizing Weights.mp4 9.7 MB
  • 11. AWS Machine Learning Service/12. Binary Classification Introduction.mp4 9.6 MB
  • 18. Logistic Regression/6. Lab Cost Example.mp4 9.6 MB
  • 19. Onset of Diabetes Prediction/2. Lab Prepare For Training.mp4 9.0 MB
  • 22. Data Transformation using Recipes/1. Recipe Overview.mp4 8.9 MB
  • 16. Adding Complex Features/1. Lab Prepare Training Data.mp4 8.5 MB
  • 18. Logistic Regression/5. Lab Logistic Cost Function.mp4 7.8 MB
  • 24. Integration of AWS Machine Learning With Your Application/3. Security using IAM.mp4 7.7 MB
  • 1. Introduction and Housekeeping/5. AWS Command Line Interface Tool Setup and Summary.mp4 7.6 MB
  • 11. AWS Machine Learning Service/9. Machine Learning Terminology.mp4 7.4 MB
  • 18. Logistic Regression/8. Summary.mp4 7.4 MB
  • 22. Data Transformation using Recipes/5. Numeric Transformation - Normalization.mp4 7.3 MB
  • 16. Adding Complex Features/4. Lab Performance Of Model With Degree 1 Features.mp4 7.3 MB
  • 6. SageMaker - Principal Component Analysis (PCA)/12. Summary.mp4 7.0 MB
  • 16. Adding Complex Features/5. Lab Performance of Model with Degree 4 Features.mp4 6.9 MB
  • 20. Multiclass Classifiers using Multinomial Logistic Regression/8. Summary.mp4 6.9 MB
  • 19. Onset of Diabetes Prediction/8. Lab Cutoff Threshold Interactive Testing.mp4 6.5 MB
  • 11. AWS Machine Learning Service/13. Multiclass Classification Introduction.mp4 6.3 MB
  • 1. Introduction and Housekeeping/2. Root Account Setup and Billing Dashboard Overview.mp4 6.2 MB
  • 23. Hyper Parameters, Model Optimization and Lifecycle/4. Regularization Effect.mp4 6.2 MB
  • 23. Hyper Parameters, Model Optimization and Lifecycle/3. Regularization, Learning Rate.mp4 6.0 MB
  • 11. AWS Machine Learning Service/10. Data Types supported by AWS Machine Learning.mp4 5.6 MB
  • 24. Integration of AWS Machine Learning With Your Application/1. Introduction.mp4 5.6 MB
  • 6. SageMaker - Principal Component Analysis (PCA)/3. PCA Demo Overview.mp4 5.3 MB
  • 13. AWS - Linear Regression Models/9. Model Performance Summary and Conclusion.mp4 5.3 MB
  • 20. Multiclass Classifiers using Multinomial Logistic Regression/3. Concept Evaluating Predictive Quality of Multiclass Classifiers.mp4 5.2 MB
  • 23. Hyper Parameters, Model Optimization and Lifecycle/8. AWS Machine Learning Pricing.mp4 5.2 MB
  • 24. Integration of AWS Machine Learning With Your Application/4. Hands-on lab - List of Demos and Objective.mp4 5.1 MB
  • 16. Adding Complex Features/2. Lab Adding Complex Features.mp4 5.0 MB
  • 22. Data Transformation using Recipes/4. Numeric Transformation - Quantile Binning.mp4 4.9 MB
  • 24. Integration of AWS Machine Learning With Your Application/2. Integration Scenarios.mp4 4.8 MB
  • 13. AWS - Linear Regression Models/7. Lab Review Default Recipe Settings Used To Train model.mp4 4.8 MB
  • 19. Onset of Diabetes Prediction/11. Summary.mp4 4.6 MB
  • 23. Hyper Parameters, Model Optimization and Lifecycle/7. AWS Machine Learning System Limits.mp4 4.5 MB
  • 19. Onset of Diabetes Prediction/6. Concept AUC Metric.mp4 4.4 MB
  • 22. Data Transformation using Recipes/6. Cartesian Product Transformation - Categorical and Text.mp4 4.1 MB
  • 12. Linear Regression/3. Summary.mp4 4.1 MB
  • 16. Adding Complex Features/6. Lab Performance of Model With Degree 15 Features.mp4 3.9 MB
  • 16. Adding Complex Features/7. Summary.mp4 3.8 MB
  • 1. Introduction and Housekeeping/1. Introduction.mp4 3.8 MB
  • 24. Integration of AWS Machine Learning With Your Application/11. Cognito Overview.mp4 3.8 MB
  • 24. Integration of AWS Machine Learning With Your Application/10. Demo Allowing Prediction Only For Registered Users.mp4 3.7 MB
  • 17. Kaggle Bike Hourly Rental Prediction/4. Linear Regression Wrapup and Summary.mp4 3.6 MB
  • 14. Adding Features To Improve Model/4. Summary.mp4 3.4 MB
  • 15. Normalization/4. Summary.mp4 3.3 MB
  • 11. AWS Machine Learning Service/6. Summary.mp4 2.3 MB
  • 11. AWS Machine Learning Service/3.1 ProjectSetup.zip.zip 1.7 MB
  • 21. Text Based Classification with AWS Twitter Dataset/4. Logistic Regression Summary.mp4 1.6 MB
  • 26. Conclusion/2. Conclusion.mp4 1.3 MB
  • 23. Hyper Parameters, Model Optimization and Lifecycle/1. Introduction.mp4 1.1 MB
  • 24. Integration of AWS Machine Learning With Your Application/15. Summary.mp4 905.8 kB
  • 22. Data Transformation using Recipes/7. Summary.mp4 729.0 kB
  • 25. Homework - Additional Problems/1.1 MushroomData_Class.csv.csv 382.1 kB
  • 2. 2019 SageMaker Housekeeping/1.2 AWS Introduction ML Concepts.pdf.pdf 273.3 kB
  • 4. 2019 SageMaker Service Overview/1.1 AWS SageMaker_WM.pdf.pdf 226.6 kB
  • 9. 2019 Integration Options - Model Endpoint/1.2 Local Machine - Housekeeping.pdf.pdf 205.7 kB
  • 9. 2019 Integration Options - Model Endpoint/4.1 Local Machine - Housekeeping.pdf.pdf 205.7 kB
  • 2. 2019 SageMaker Housekeeping/1.1 SourceCode and Data Setup.pdf.pdf 183.7 kB
  • 8. SageMaker - DeepAR Time Series Forecasting/1.1 AWS SageMakerDeepAR_WM.pdf.pdf 180.2 kB
  • 7. SageMaker - Factorization Machines/1.1 2. AWS SageMakerFactorizationMachine_WM.pdf.pdf 137.0 kB
  • 6. SageMaker - Principal Component Analysis (PCA)/1.1 AWS SageMakerPCA_WM.pdf.pdf 121.5 kB
  • 9. 2019 Integration Options - Model Endpoint/1.1 AWS SageMaker Integration.pdf.pdf 119.4 kB
  • 1. Introduction and Housekeeping/2.1 AWS HouseKeeping_WM.pdf.pdf 115.6 kB
  • 1. Introduction and Housekeeping/6.1 2018 AWSTop6ReasonsCloudComputing.pdf.pdf 107.9 kB
  • 10. 2019 SageMaker HyperParameter Tuning/1.1 AWS SageMaker Hyperparameter Tuning.pdf.pdf 72.7 kB
  • 1. Introduction and Housekeeping/1.1 Machine Learning at AWS Introduction_WM.pdf.pdf 64.3 kB
  • 5. XGBoost - Gradient Boosted Trees/13.1 20. How to remove SageMaker endpoints and Shutdown Notebook Instance.pdf.pdf 29.2 kB
  • 10. 2019 SageMaker HyperParameter Tuning/1.2 FM-Autotuning-Lab-Configuration.xlsx.xlsx 10.9 kB
  • 26. Conclusion/1. BONUS Learn Advanced Data Processing Techniques, Cloud Computing and More.html 8.7 kB
  • 11. AWS Machine Learning Service/8. Optional Machine Learning Where To Start (Article).html 6.6 kB
  • 25. Homework - Additional Problems/1. Mushroom Classification.html 972 Bytes
  • 5. XGBoost - Gradient Boosted Trees/13. How to remove SageMaker endpoints and Shutdown Notebook Instance.html 903 Bytes
  • 6. SageMaker - Principal Component Analysis (PCA)/6. Cleanup Resources on SageMaker.html 903 Bytes
  • 11. AWS Machine Learning Service/1. 2019 MARCH - Important Update AWS Machine Learning Service Deprecated.html 837 Bytes
  • 6. SageMaker - Principal Component Analysis (PCA)/11. Exercise Kaggle Bike Train and PCA.html 720 Bytes
  • 24. Integration of AWS Machine Learning With Your Application/6.1 aws_ml_command_line.txt.txt 635 Bytes
  • 5. XGBoost - Gradient Boosted Trees/14. Creating EndPoint From Existing Model Artifacts.html 476 Bytes
  • 7. SageMaker - Factorization Machines/1. Downloadable Resources.html 290 Bytes
  • 7. SageMaker - Factorization Machines/3. MovieLens Dataset.html 281 Bytes
  • 6. SageMaker - Principal Component Analysis (PCA)/1. Downloadable Resources.html 273 Bytes
  • 8. SageMaker - DeepAR Time Series Forecasting/1. Downloadable Resources.html 273 Bytes
  • 4. 2019 SageMaker Service Overview/1. Downloadable Resources.html 272 Bytes
  • 2. 2019 SageMaker Housekeeping/1. Downloadable Resources.html 166 Bytes
  • 10. 2019 SageMaker HyperParameter Tuning/1. Downloadable Resources.html 159 Bytes
  • 9. 2019 Integration Options - Model Endpoint/4. Install SageMaker SDK, GIT Client, Source Code, Security Permissions.html 154 Bytes
  • 11. AWS Machine Learning Service/15. Algorithm and Terminology Quiz.html 152 Bytes
  • 11. AWS Machine Learning Service/7. Introduction and House Keeping Quiz.html 152 Bytes
  • 12. Linear Regression/4. Linear Regression Quiz.html 152 Bytes
  • 13. AWS - Linear Regression Models/10. AWS Regression Metrics Quiz.html 152 Bytes
  • 13. AWS - Linear Regression Models/4. AWS Models Quiz.html 152 Bytes
  • 15. Normalization/5. Underfitting and Normalization Quiz.html 152 Bytes
  • 18. Logistic Regression/9. Logistic Regression Quiz.html 152 Bytes
  • 19. Onset of Diabetes Prediction/12. Logistic Regression Metrics Quiz.html 152 Bytes
  • 9. 2019 Integration Options - Model Endpoint/1. Downloadable Resources.html 145 Bytes
  • [Tutorialsplanet.NET].url 128 Bytes

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