AWS SageMaker Practical for Beginners. Build 6 Projects

磁力链接/BT种子名称

AWS SageMaker Practical for Beginners. Build 6 Projects

磁力链接/BT种子简介

种子哈希：1f373a4d53f676fc15686f8f75a02e9cd66d165a
文件大小： 8.98G
已经下载：4242次
下载速度：极快
收录时间：2021-03-18
最近下载：2026-05-30
DMCA/投诉/Complaint：DMCA/投诉/Complaint

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文件列表

  • 3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/30 - Coding Task #7 - Train a Linear Learner Model in AWS SageMaker.mp4 507.2 MB
  • 4 - Project #2 - Medical Insurance Premium Prediction/41 - Coding Task #7 - Train a Linear Learner Model in AWS SageMaker.mp4 361.2 MB
  • 4 - Project #2 - Medical Insurance Premium Prediction/49 - Coding Task #9 - Train Artificial Neural Networks for Regression Tasks.mp4 262.4 MB
  • 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/81 - Coding Task #6 - Train & Test XGboost and Perform Grid Search (Local Mode).mp4 240.1 MB
  • 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/61 - Coding Task #1 #2 #3 - Load Dataset_Libraries and Perform Data Exploration.mp4 236.3 MB
  • 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/75 - Precision, Recall, and F1-Score.mp4 217.4 MB
  • 7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker/95 - Coding Task #5 - Build and Train CNNs.mp4 216.1 MB
  • 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/64 - Coding Task #6 #7 - Visualize Dataset.mp4 215.1 MB
  • 3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/23 - Coding Task #1A - Instantiate AWS SageMaker Notebook Instance (Method #1).mp4 204.9 MB
  • 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/57 - Gradient Boosted Trees - Deep Dive - Part #1.mp4 188.5 MB
  • 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/67 - Coding Task #10 - Train XGBoost Using SageMaker.mp4 184.5 MB
  • 3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/22 - AWS SageMaker Linear Learner Overview.mp4 176.4 MB
  • 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/69 - Coding Task #12 - Perform Hyperparameters Tuning.mp4 174.5 MB
  • 4 - Project #2 - Medical Insurance Premium Prediction/37 - Coding Task #3 - Perform Exploratory Data Analysis.mp4 165.9 MB
  • 7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker/93 - Coding Part #1 #2 - Import Images and Visualize Them.mp4 165.5 MB
  • 1 - Introduction, Success Tips & Best Practices and Key Learning Outcomes/04 - Course Outline and Key Learning Outcomes.mp4 163.7 MB
  • 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/82 - Coding Task #7 - Train a PCA Model in AWS SageMaker.mp4 163.2 MB
  • SageMaker+Practical+Course+Package.zip 152.2 MB
  • 3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/26 - Coding Task #3 - Perform Exploratory Data Analysis.mp4 151.0 MB
  • 4 - Project #2 - Medical Insurance Premium Prediction/36 - Coding Task #1 & #2 - Import Dataset and Key Libraries.mp4 142.7 MB
  • 2 - Introduction to AI_ML, AWS and Cloud Computing/18 - SageMaker Models Deployment.mp4 140.3 MB
  • 3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/31 - Coding Task #8 - Deploy Model & invoke endpoint in SageMaker.mp4 131.2 MB
  • 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.mp4 130.3 MB
  • 2 - Introduction to AI_ML, AWS and Cloud Computing/15 - AWS SageMaker Walk-through.mp4 123.9 MB
  • 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.mp4 123.8 MB
  • 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/84 - Coding Task #9 - Train XGBoost (SageMaker Built-in) to do Classification Tasks.mp4 121.1 MB
  • 4 - Project #2 - Medical Insurance Premium Prediction/38 - Coding Task #4 - Perform Data Visualization.mp4 118.2 MB
  • 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/72 - Principal Component Analysis (PCA) Intuition.mp4 117.2 MB
  • 4 - Project #2 - Medical Insurance Premium Prediction/42 - Coding Task #8 - Deploy Trained Model and Invoke Endpoint.mp4 116.8 MB
  • 2 - Introduction to AI_ML, AWS and Cloud Computing/07 - Introduction to AI, Machine Learning and Deep Learning - Part #2.mp4 116.5 MB
  • 2 - Introduction to AI_ML, AWS and Cloud Computing/06 - Introduction to AI, Machine Learning and Deep Learning.mp4 111.6 MB
  • 4 - Project #2 - Medical Insurance Premium Prediction/47 - Gradient Descent Algorithm.mp4 110.8 MB
  • 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/60 - Project Introduction and Notebook Instance Instantiation.mp4 110.2 MB
  • 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/71 - Introduction and Project Overview.mp4 103.3 MB
  • 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/70 - Coding Task #13 - Retrain the Model Using best (optimized) Hyperparameters.mp4 102.3 MB
  • 7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker/86 - Project Overview and Introduction.mp4 101.5 MB
  • 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/83 - Coding Task #8 - Deploy Trained PCA Model Endpoint & Envoke endpoint.mp4 98.3 MB
  • 3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/27 - Coding Task #4 - Create Training and Testing Dataset.mp4 96.4 MB
  • 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/80 - Coding Task #4 & #5 - Visualize Datasets & Prepare Training_Testing Data.mp4 95.2 MB
  • 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/79 - Coding Task #2 & #3 - Import Data_Libraries & Perform Exploratory data analysis.mp4 93.4 MB
  • 2 - Introduction to AI_ML, AWS and Cloud Computing/12 - Amazon S3.mp4 93.2 MB
  • 3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/24 - Coding Task #1B - Using AWS SageMaker Studio (Method #2).mp4 92.7 MB
  • 7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker/91 - LeNet Network Architecture.mp4 89.9 MB
  • 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/66 - Coding Task #9 - Train XGBoost Locally.mp4 88.9 MB
  • 4 - Project #2 - Medical Insurance Premium Prediction/34 - Regression Metrics and KPIs - RMSE, MSE, MAE, MAPE.mp4 87.5 MB
  • 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/85 - Coding Task #10 - Deploy Endpoint, Make Inference @ Test Model.mp4 87.1 MB
  • 4 - Project #2 - Medical Insurance Premium Prediction/35 - Regression Metrics and KPIs - R2 and Adjusted R2.mp4 87.0 MB
  • 2 - Introduction to AI_ML, AWS and Cloud Computing/13 - Amazon EC2 and IAM.mp4 86.8 MB
  • 2 - Introduction to AI_ML, AWS and Cloud Computing/17 - AWS SageMaker Studio Walk-through.mp4 81.5 MB
  • 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/58 - Gradient Boosted Trees - Deep Dive - Part #2.mp4 80.2 MB
  • 4 - Project #2 - Medical Insurance Premium Prediction/39 - Coding Task #5 - Create Training and Testing Datasets.mp4 79.4 MB
  • 3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/28 - Coding Task #5 - Train a Linear Regression Model in SkLearn.mp4 77.9 MB
  • 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/62 - Coding Task #4 - Merge and Manipulate DataFrame Using Pandas.mp4 77.2 MB
  • 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/50 - Introduction to Case Study.mp4 76.7 MB
  • 2 - Introduction to AI_ML, AWS and Cloud Computing/09 - Introduction to AWS and Cloud Computing.mp4 74.8 MB
  • 7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker/96 - Coding Task #6 - Deploy Trained Model Using SageMaker.mp4 73.9 MB
  • 4 - Project #2 - Medical Insurance Premium Prediction/43 - Artificial Neural Networks for Regression Tasks.mp4 73.6 MB
  • 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/68 - Coding Task #11 - Deploy XGBoost endpoint and Make Predictions.mp4 72.5 MB
  • 3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/25 - Coding Task #2 - Import Key libraries and dataset.mp4 70.8 MB
  • 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/51 - Basics - What is the difference between Bias & Variance.mp4 69.6 MB
  • 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/63 - Coding Task #5 - Explore Merged Datasets.mp4 66.2 MB
  • 3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/29 - Coding Task #6 - Evaluate Trained Model Performance.mp4 65.8 MB
  • 3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/20 - Simple Linear Regression Intuition.mp4 63.2 MB
  • 4 - Project #2 - Medical Insurance Premium Prediction/40 - Coding Task #6 - Train a Machine Learning Model Locally.mp4 60.7 MB
  • 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/78 - Coding Task #1 - SageMaker Studio Notebook Setup.mp4 60.7 MB
  • 2 - Introduction to AI_ML, AWS and Cloud Computing/11 - AWS Regions and Availability Zones.mp4 60.5 MB
  • 7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker/94 - Coding #3 #4 - Upload Training_Testing Data to S3.mp4 58.8 MB
  • 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/59 - AWS SageMaker XGBoost Algorithm.mp4 58.5 MB
  • 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/73 - XGBoost for Classification Tasks (Review Lecture).mp4 57.7 MB
  • 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/74 - Confusion Matrix.mp4 56.1 MB
  • 3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/21 - Least Sum of Squares.mp4 54.7 MB
  • 1 - Introduction, Success Tips & Best Practices and Key Learning Outcomes/03 - Course Key Tips and Best Practices.mp4 53.6 MB
  • 2 - Introduction to AI_ML, AWS and Cloud Computing/16 - AWS SageMaker Studio Overview.mp4 50.0 MB
  • 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/55 - What is Boosting.mp4 48.8 MB
  • 2 - Introduction to AI_ML, AWS and Cloud Computing/08 - Good Data Vs. Bad Data.mp4 48.7 MB
  • 2 - Introduction to AI_ML, AWS and Cloud Computing/10 - Key Machine Learning Components and AWS Management Console Tour.mp4 44.1 MB
  • 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/76 - Area Under Curve (AUC) and Receiver Operating Characteristics (ROC) Metrics.mp4 44.0 MB
  • 4 - Project #2 - Medical Insurance Premium Prediction/46 - How do Artificial Neural Networks Train.mp4 43.5 MB
  • 7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker/90 - Confusion Matrix.mp4 42.4 MB
  • 2 - Introduction to AI_ML, AWS and Cloud Computing/14 - AWS SageMaker Overview.mp4 40.3 MB
  • 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/56 - Decision Trees and Ensemble Learning.mp4 37.7 MB
  • 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/54 - Introduction to XGBoost (Extreme Gradient Boosting) algorithm.mp4 36.7 MB
  • 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/65 - Coding Task #8 - Prepare the Data To Perform Training.mp4 35.2 MB
  • 2 - Introduction to AI_ML, AWS and Cloud Computing/05 - AWS Free Tier Account Setup and Overview.mp4 34.6 MB
  • 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/52 - Basics - L1 & L2 Regularization - Part #1.mp4 33.7 MB
  • 1 - Introduction, Success Tips & Best Practices and Key Learning Outcomes/01 - Course Introduction and Welcome Message.mp4 25.7 MB
  • 4 - Project #2 - Medical Insurance Premium Prediction/48 - Backpropagation Algorithm.mp4 23.9 MB
  • 3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner/19 - Project Overview.mp4 22.4 MB
  • 4 - Project #2 - Medical Insurance Premium Prediction/33 - Multiple Linear Regression Intuition.mp4 21.8 MB
  • 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)/77 - Overfitting and Under fitting Models.mp4 21.2 MB
  • 4 - Project #2 - Medical Insurance Premium Prediction/44 - Activation Functions - Sigmoid, RELU and Tanh.mp4 21.0 MB
  • 4 - Project #2 - Medical Insurance Premium Prediction/45 - Multilayer Perceptron Networks.mp4 20.6 MB
  • 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)/53 - Basics - L1 & L2 Regularization - Part #2.mp4 17.0 MB
  • 7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker/89 - How to Improve CNNs Performance.mp4 13.9 MB
  • 4 - Project #2 - Medical Insurance Premium Prediction/32 - Project Overview and Introduction.mp4 12.0 MB
  • 1 - Introduction, Success Tips & Best Practices and Key Learning Outcomes/02 - Updates on Udemy Reviews.mp4 6.2 MB
  • 7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker/92 - Request AWS SageMaker Service Limit Increase.mp4 5.2 MB

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