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种子名称： AWS SageMaker Practical for Beginners. Build 6 Projects
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文件数目： 96个文件
文件大小： 8.84 GB
收录时间： 2021-11-27 22:59
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AWS SageMaker Practical for Beginners. Build 6 Projects.torrent

1 - Introduction, Success Tips & Best Practices and Key Learning Outcomes/01 - Course Introduction and Welcome Message.mp424.54MB

1 - Introduction, Success Tips & Best Practices and Key Learning Outcomes/02 - Updates on Udemy Reviews.mp45.93MB

1 - Introduction, Success Tips & Best Practices and Key Learning Outcomes/03 - Course Key Tips and Best Practices.mp451.13MB

1 - Introduction, Success Tips & Best Practices and Key Learning Outcomes/04 - Course Outline and Key Learning Outcomes.mp4156.07MB

2 - Introduction to AI_ML, AWS and Cloud Computing/05 - AWS Free Tier Account Setup and Overview.mp433.02MB

2 - Introduction to AI_ML, AWS and Cloud Computing/06 - Introduction to AI, Machine Learning and Deep Learning.mp4106.47MB

2 - Introduction to AI_ML, AWS and Cloud Computing/07 - Introduction to AI, Machine Learning and Deep Learning - Part #2.mp4111.11MB

2 - Introduction to AI_ML, AWS and Cloud Computing/08 - Good Data Vs. Bad Data.mp446.46MB

2 - Introduction to AI_ML, AWS and Cloud Computing/09 - Introduction to AWS and Cloud Computing.mp471.38MB

2 - Introduction to AI_ML, AWS and Cloud Computing/10 - Key Machine Learning Components and AWS Management Console Tour.mp442.05MB

2 - Introduction to AI_ML, AWS and Cloud Computing/11 - AWS Regions and Availability Zones.mp457.71MB

2 - Introduction to AI_ML, AWS and Cloud Computing/12 - Amazon S3.mp488.88MB

2 - Introduction to AI_ML, AWS and Cloud Computing/13 - Amazon EC2 and IAM.mp482.78MB

2 - Introduction to AI_ML, AWS and Cloud Computing/14 - AWS SageMaker Overview.mp438.44MB

2 - Introduction to AI_ML, AWS and Cloud Computing/15 - AWS SageMaker Walk-through.mp4118.16MB

2 - Introduction to AI_ML, AWS and Cloud Computing/16 - AWS SageMaker Studio Overview.mp447.64MB

2 - Introduction to AI_ML, AWS and Cloud Computing/17 - AWS SageMaker Studio Walk-through.mp477.75MB

2 - Introduction to AI_ML, AWS and Cloud Computing/18 - SageMaker Models Deployment.mp4133.81MB

3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/19 - Project Overview.mp421.34MB

3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/20 - Simple Linear Regression Intuition.mp460.3MB

3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/21 - Least Sum of Squares.mp452.16MB

3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/22 - AWS SageMaker Linear Learner Overview.mp4168.2MB

3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/23 - Coding Task #1A - Instantiate AWS SageMaker Notebook Instance (Method #1).mp4195.42MB

3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/24 - Coding Task #1B - Using AWS SageMaker Studio (Method #2).mp488.43MB

3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/25 - Coding Task #2 - Import Key libraries and dataset.mp467.48MB

3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/26 - Coding Task #3 - Perform Exploratory Data Analysis.mp4144.04MB

3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/27 - Coding Task #4 - Create Training and Testing Dataset.mp491.98MB

3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/28 - Coding Task #5 - Train a Linear Regression Model in SkLearn.mp474.29MB

3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/29 - Coding Task #6 - Evaluate Trained Model Performance.mp462.73MB

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

3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/31 - Coding Task #8 - Deploy Model & invoke endpoint in SageMaker.mp4125.12MB

4 - Project #2 - Medical Insurance Premium Prediction/32 - Project Overview and Introduction.mp411.44MB

4 - Project #2 - Medical Insurance Premium Prediction/33 - Multiple Linear Regression Intuition.mp420.82MB

4 - Project #2 - Medical Insurance Premium Prediction/34 - Regression Metrics and KPIs - RMSE, MSE, MAE, MAPE.mp483.4MB

4 - Project #2 - Medical Insurance Premium Prediction/35 - Regression Metrics and KPIs - R2 and Adjusted R2.mp483.01MB

4 - Project #2 - Medical Insurance Premium Prediction/36 - Coding Task #1 & #2 - Import Dataset and Key Libraries.mp4136.07MB

4 - Project #2 - Medical Insurance Premium Prediction/37 - Coding Task #3 - Perform Exploratory Data Analysis.mp4158.23MB

4 - Project #2 - Medical Insurance Premium Prediction/38 - Coding Task #4 - Perform Data Visualization.mp4112.73MB

4 - Project #2 - Medical Insurance Premium Prediction/39 - Coding Task #5 - Create Training and Testing Datasets.mp475.77MB

4 - Project #2 - Medical Insurance Premium Prediction/40 - Coding Task #6 - Train a Machine Learning Model Locally.mp457.93MB

4 - Project #2 - Medical Insurance Premium Prediction/41 - Coding Task #7 - Train a Linear Learner Model in AWS SageMaker.mp4344.44MB

4 - Project #2 - Medical Insurance Premium Prediction/42 - Coding Task #8 - Deploy Trained Model and Invoke Endpoint.mp4111.42MB

4 - Project #2 - Medical Insurance Premium Prediction/43 - Artificial Neural Networks for Regression Tasks.mp470.23MB

4 - Project #2 - Medical Insurance Premium Prediction/44 - Activation Functions - Sigmoid, RELU and Tanh.mp420.03MB

4 - Project #2 - Medical Insurance Premium Prediction/45 - Multilayer Perceptron Networks.mp419.64MB

4 - Project #2 - Medical Insurance Premium Prediction/46 - How do Artificial Neural Networks Train.mp441.44MB

4 - Project #2 - Medical Insurance Premium Prediction/47 - Gradient Descent Algorithm.mp4105.63MB

4 - Project #2 - Medical Insurance Premium Prediction/48 - Backpropagation Algorithm.mp422.82MB

4 - Project #2 - Medical Insurance Premium Prediction/49 - Coding Task #9 - Train Artificial Neural Networks for Regression Tasks.mp4250.2MB

5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/50 - Introduction to Case Study.mp473.16MB

5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/51 - Basics - What is the difference between Bias & Variance.mp466.42MB

5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/52 - Basics - L1 & L2 Regularization - Part #1.mp432.15MB

5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/53 - Basics - L1 & L2 Regularization - Part #2.mp416.24MB

5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/54 - Introduction to XGBoost (Extreme Gradient Boosting) algorithm.mp434.99MB

5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/55 - What is Boosting.mp446.54MB

5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/56 - Decision Trees and Ensemble Learning.mp435.98MB

5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/57 - Gradient Boosted Trees - Deep Dive - Part #1.mp4179.8MB

5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/58 - Gradient Boosted Trees - Deep Dive - Part #2.mp476.52MB

5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/59 - AWS SageMaker XGBoost Algorithm.mp455.81MB

5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/60 - Project Introduction and Notebook Instance Instantiation.mp4105.05MB

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

5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/62 - Coding Task #4 - Merge and Manipulate DataFrame Using Pandas.mp473.62MB

5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/63 - Coding Task #5 - Explore Merged Datasets.mp463.18MB

5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/64 - Coding Task #6 #7 - Visualize Dataset.mp4205.17MB

5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/65 - Coding Task #8 - Prepare the Data To Perform Training.mp433.6MB

5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/66 - Coding Task #9 - Train XGBoost Locally.mp484.81MB

5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/67 - Coding Task #10 - Train XGBoost Using SageMaker.mp4175.95MB

5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/68 - Coding Task #11 - Deploy XGBoost endpoint and Make Predictions.mp469.15MB

5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/69 - Coding Task #12 - Perform Hyperparameters Tuning.mp4166.42MB

5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/70 - Coding Task #13 - Retrain the Model Using best (optimized) Hyperparameters.mp497.57MB

6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/71 - Introduction and Project Overview.mp498.53MB

6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/72 - Principal Component Analysis (PCA) Intuition.mp4111.75MB

6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/73 - XGBoost for Classification Tasks (Review Lecture).mp454.98MB

6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/74 - Confusion Matrix.mp453.47MB

6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/75 - Precision, Recall, and F1-Score.mp4207.3MB

6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/76 - Area Under Curve (AUC) and Receiver Operating Characteristics (ROC) Metrics.mp441.98MB

6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/77 - Overfitting and Under fitting Models.mp420.23MB

6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/78 - Coding Task #1 - SageMaker Studio Notebook Setup.mp457.87MB

6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/79 - Coding Task #2 & #3 - Import Data_Libraries & Perform Exploratory data analysis.mp489.06MB

6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/80 - Coding Task #4 & #5 - Visualize Datasets & Prepare Training_Testing Data.mp490.84MB

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

6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/82 - Coding Task #7 - Train a PCA Model in AWS SageMaker.mp4155.61MB

6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/83 - Coding Task #8 - Deploy Trained PCA Model Endpoint & Envoke endpoint.mp493.77MB

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

6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/85 - Coding Task #10 - Deploy Endpoint, Make Inference @ Test Model.mp483.07MB

7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker/86 - Project Overview and Introduction.mp496.8MB

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

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

7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker/89 - How to Improve CNNs Performance.mp413.27MB

7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker/90 - Confusion Matrix.mp440.48MB

7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker/91 - LeNet Network Architecture.mp485.72MB

7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker/92 - Request AWS SageMaker Service Limit Increase.mp44.99MB

7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker/93 - Coding Part #1 #2 - Import Images and Visualize Them.mp4157.85MB

7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker/94 - Coding #3 #4 - Upload Training_Testing Data to S3.mp456.04MB

7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker/95 - Coding Task #5 - Build and Train CNNs.mp4206.11MB

7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker/96 - Coding Task #6 - Deploy Trained Model Using SageMaker.mp470.47MB