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AWS SageMaker Practical for Beginners. Build 6 Projects

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AWS SageMaker Practical for Beginners Build 6 Projects

文件列表
  1. 1 - Introduction, Success Tips & Best Practices and Key Learning Outcomes/01 - Course Introduction and Welcome Message.mp424.54MB
  2. 1 - Introduction, Success Tips & Best Practices and Key Learning Outcomes/02 - Updates on Udemy Reviews.mp45.93MB
  3. 1 - Introduction, Success Tips & Best Practices and Key Learning Outcomes/03 - Course Key Tips and Best Practices.mp451.13MB
  4. 1 - Introduction, Success Tips & Best Practices and Key Learning Outcomes/04 - Course Outline and Key Learning Outcomes.mp4156.07MB
  5. 2 - Introduction to AI_ML, AWS and Cloud Computing/05 - AWS Free Tier Account Setup and Overview.mp433.02MB
  6. 2 - Introduction to AI_ML, AWS and Cloud Computing/06 - Introduction to AI, Machine Learning and Deep Learning.mp4106.47MB
  7. 2 - Introduction to AI_ML, AWS and Cloud Computing/07 - Introduction to AI, Machine Learning and Deep Learning - Part #2.mp4111.11MB
  8. 2 - Introduction to AI_ML, AWS and Cloud Computing/08 - Good Data Vs. Bad Data.mp446.46MB
  9. 2 - Introduction to AI_ML, AWS and Cloud Computing/09 - Introduction to AWS and Cloud Computing.mp471.38MB
  10. 2 - Introduction to AI_ML, AWS and Cloud Computing/10 - Key Machine Learning Components and AWS Management Console Tour.mp442.05MB
  11. 2 - Introduction to AI_ML, AWS and Cloud Computing/11 - AWS Regions and Availability Zones.mp457.71MB
  12. 2 - Introduction to AI_ML, AWS and Cloud Computing/12 - Amazon S3.mp488.88MB
  13. 2 - Introduction to AI_ML, AWS and Cloud Computing/13 - Amazon EC2 and IAM.mp482.78MB
  14. 2 - Introduction to AI_ML, AWS and Cloud Computing/14 - AWS SageMaker Overview.mp438.44MB
  15. 2 - Introduction to AI_ML, AWS and Cloud Computing/15 - AWS SageMaker Walk-through.mp4118.16MB
  16. 2 - Introduction to AI_ML, AWS and Cloud Computing/16 - AWS SageMaker Studio Overview.mp447.64MB
  17. 2 - Introduction to AI_ML, AWS and Cloud Computing/17 - AWS SageMaker Studio Walk-through.mp477.75MB
  18. 2 - Introduction to AI_ML, AWS and Cloud Computing/18 - SageMaker Models Deployment.mp4133.81MB
  19. 3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/19 - Project Overview.mp421.34MB
  20. 3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/20 - Simple Linear Regression Intuition.mp460.3MB
  21. 3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/21 - Least Sum of Squares.mp452.16MB
  22. 3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/22 - AWS SageMaker Linear Learner Overview.mp4168.2MB
  23. 3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/23 - Coding Task #1A - Instantiate AWS SageMaker Notebook Instance (Method #1).mp4195.42MB
  24. 3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/24 - Coding Task #1B - Using AWS SageMaker Studio (Method #2).mp488.43MB
  25. 3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/25 - Coding Task #2 - Import Key libraries and dataset.mp467.48MB
  26. 3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/26 - Coding Task #3 - Perform Exploratory Data Analysis.mp4144.04MB
  27. 3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/27 - Coding Task #4 - Create Training and Testing Dataset.mp491.98MB
  28. 3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/28 - Coding Task #5 - Train a Linear Regression Model in SkLearn.mp474.29MB
  29. 3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/29 - Coding Task #6 - Evaluate Trained Model Performance.mp462.73MB
  30. 3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/30 - Coding Task #7 - Train a Linear Learner Model in AWS SageMaker.mp4483.72MB
  31. 3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/31 - Coding Task #8 - Deploy Model & invoke endpoint in SageMaker.mp4125.12MB
  32. 4 - Project #2 - Medical Insurance Premium Prediction/32 - Project Overview and Introduction.mp411.44MB
  33. 4 - Project #2 - Medical Insurance Premium Prediction/33 - Multiple Linear Regression Intuition.mp420.82MB
  34. 4 - Project #2 - Medical Insurance Premium Prediction/34 - Regression Metrics and KPIs - RMSE, MSE, MAE, MAPE.mp483.4MB
  35. 4 - Project #2 - Medical Insurance Premium Prediction/35 - Regression Metrics and KPIs - R2 and Adjusted R2.mp483.01MB
  36. 4 - Project #2 - Medical Insurance Premium Prediction/36 - Coding Task #1 & #2 - Import Dataset and Key Libraries.mp4136.07MB
  37. 4 - Project #2 - Medical Insurance Premium Prediction/37 - Coding Task #3 - Perform Exploratory Data Analysis.mp4158.23MB
  38. 4 - Project #2 - Medical Insurance Premium Prediction/38 - Coding Task #4 - Perform Data Visualization.mp4112.73MB
  39. 4 - Project #2 - Medical Insurance Premium Prediction/39 - Coding Task #5 - Create Training and Testing Datasets.mp475.77MB
  40. 4 - Project #2 - Medical Insurance Premium Prediction/40 - Coding Task #6 - Train a Machine Learning Model Locally.mp457.93MB
  41. 4 - Project #2 - Medical Insurance Premium Prediction/41 - Coding Task #7 - Train a Linear Learner Model in AWS SageMaker.mp4344.44MB
  42. 4 - Project #2 - Medical Insurance Premium Prediction/42 - Coding Task #8 - Deploy Trained Model and Invoke Endpoint.mp4111.42MB
  43. 4 - Project #2 - Medical Insurance Premium Prediction/43 - Artificial Neural Networks for Regression Tasks.mp470.23MB
  44. 4 - Project #2 - Medical Insurance Premium Prediction/44 - Activation Functions - Sigmoid, RELU and Tanh.mp420.03MB
  45. 4 - Project #2 - Medical Insurance Premium Prediction/45 - Multilayer Perceptron Networks.mp419.64MB
  46. 4 - Project #2 - Medical Insurance Premium Prediction/46 - How do Artificial Neural Networks Train.mp441.44MB
  47. 4 - Project #2 - Medical Insurance Premium Prediction/47 - Gradient Descent Algorithm.mp4105.63MB
  48. 4 - Project #2 - Medical Insurance Premium Prediction/48 - Backpropagation Algorithm.mp422.82MB
  49. 4 - Project #2 - Medical Insurance Premium Prediction/49 - Coding Task #9 - Train Artificial Neural Networks for Regression Tasks.mp4250.2MB
  50. 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/50 - Introduction to Case Study.mp473.16MB
  51. 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/51 - Basics - What is the difference between Bias & Variance.mp466.42MB
  52. 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/52 - Basics - L1 & L2 Regularization - Part #1.mp432.15MB
  53. 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/53 - Basics - L1 & L2 Regularization - Part #2.mp416.24MB
  54. 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/54 - Introduction to XGBoost (Extreme Gradient Boosting) algorithm.mp434.99MB
  55. 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/55 - What is Boosting.mp446.54MB
  56. 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/56 - Decision Trees and Ensemble Learning.mp435.98MB
  57. 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/57 - Gradient Boosted Trees - Deep Dive - Part #1.mp4179.8MB
  58. 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/58 - Gradient Boosted Trees - Deep Dive - Part #2.mp476.52MB
  59. 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/59 - AWS SageMaker XGBoost Algorithm.mp455.81MB
  60. 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/60 - Project Introduction and Notebook Instance Instantiation.mp4105.05MB
  61. 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/61 - Coding Task #1 #2 #3 - Load Dataset_Libraries and Perform Data Exploration.mp4225.33MB
  62. 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/62 - Coding Task #4 - Merge and Manipulate DataFrame Using Pandas.mp473.62MB
  63. 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/63 - Coding Task #5 - Explore Merged Datasets.mp463.18MB
  64. 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/64 - Coding Task #6 #7 - Visualize Dataset.mp4205.17MB
  65. 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/65 - Coding Task #8 - Prepare the Data To Perform Training.mp433.6MB
  66. 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/66 - Coding Task #9 - Train XGBoost Locally.mp484.81MB
  67. 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/67 - Coding Task #10 - Train XGBoost Using SageMaker.mp4175.95MB
  68. 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/68 - Coding Task #11 - Deploy XGBoost endpoint and Make Predictions.mp469.15MB
  69. 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/69 - Coding Task #12 - Perform Hyperparameters Tuning.mp4166.42MB
  70. 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/70 - Coding Task #13 - Retrain the Model Using best (optimized) Hyperparameters.mp497.57MB
  71. 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/71 - Introduction and Project Overview.mp498.53MB
  72. 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/72 - Principal Component Analysis (PCA) Intuition.mp4111.75MB
  73. 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/73 - XGBoost for Classification Tasks (Review Lecture).mp454.98MB
  74. 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/74 - Confusion Matrix.mp453.47MB
  75. 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/75 - Precision, Recall, and F1-Score.mp4207.3MB
  76. 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/76 - Area Under Curve (AUC) and Receiver Operating Characteristics (ROC) Metrics.mp441.98MB
  77. 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/77 - Overfitting and Under fitting Models.mp420.23MB
  78. 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/78 - Coding Task #1 - SageMaker Studio Notebook Setup.mp457.87MB
  79. 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/79 - Coding Task #2 & #3 - Import Data_Libraries & Perform Exploratory data analysis.mp489.06MB
  80. 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/80 - Coding Task #4 & #5 - Visualize Datasets & Prepare Training_Testing Data.mp490.84MB
  81. 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/81 - Coding Task #6 - Train & Test XGboost and Perform Grid Search (Local Mode).mp4229.01MB
  82. 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/82 - Coding Task #7 - Train a PCA Model in AWS SageMaker.mp4155.61MB
  83. 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/83 - Coding Task #8 - Deploy Trained PCA Model Endpoint & Envoke endpoint.mp493.77MB
  84. 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/84 - Coding Task #9 - Train XGBoost (SageMaker Built-in) to do Classification Tasks.mp4115.47MB
  85. 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/85 - Coding Task #10 - Deploy Endpoint, Make Inference @ Test Model.mp483.07MB
  86. 7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker/86 - Project Overview and Introduction.mp496.8MB
  87. 7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker/87 - What are Convolutional Neural Networks and How do they Learn - Part #1.mp4118.08MB
  88. 7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker/88 - What are Convolutional Neural Networks and How do they Learn - Part #2.mp4124.29MB
  89. 7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker/89 - How to Improve CNNs Performance.mp413.27MB
  90. 7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker/90 - Confusion Matrix.mp440.48MB
  91. 7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker/91 - LeNet Network Architecture.mp485.72MB
  92. 7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker/92 - Request AWS SageMaker Service Limit Increase.mp44.99MB
  93. 7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker/93 - Coding Part #1 #2 - Import Images and Visualize Them.mp4157.85MB
  94. 7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker/94 - Coding #3 #4 - Upload Training_Testing Data to S3.mp456.04MB
  95. 7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker/95 - Coding Task #5 - Build and Train CNNs.mp4206.11MB
  96. 7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker/96 - Coding Task #6 - Deploy Trained Model Using SageMaker.mp470.47MB
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