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AWS SageMaker Practical for Beginners. Build 6 Projects
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AWS SageMaker Practical for Beginners. Build 6 Projects
1 - Introduction, Success Tips & Best Practices and Key Learning Outcomes
01 - Course Introduction and Welcome Message.mp4
- 24.54 MB
02 - Updates on Udemy Reviews.mp4
- 5.93 MB
03 - Course Key Tips and Best Practices.mp4
- 51.13 MB
04 - Course Outline and Key Learning Outcomes.mp4
- 156.07 MB
2 - Introduction to AI_ML, AWS and Cloud Computing
05 - AWS Free Tier Account Setup and Overview.mp4
- 33.02 MB
06 - Introduction to AI, Machine Learning and Deep Learning.mp4
- 106.47 MB
07 - Introduction to AI, Machine Learning and Deep Learning - Part #2.mp4
- 111.11 MB
08 - Good Data Vs. Bad Data.mp4
- 46.46 MB
09 - Introduction to AWS and Cloud Computing.mp4
- 71.38 MB
10 - Key Machine Learning Components and AWS Management Console Tour.mp4
- 42.05 MB
11 - AWS Regions and Availability Zones.mp4
- 57.71 MB
12 - Amazon S3.mp4
- 88.88 MB
13 - Amazon EC2 and IAM.mp4
- 82.78 MB
14 - AWS SageMaker Overview.mp4
- 38.44 MB
15 - AWS SageMaker Walk-through.mp4
- 118.16 MB
16 - AWS SageMaker Studio Overview.mp4
- 47.64 MB
17 - AWS SageMaker Studio Walk-through.mp4
- 77.75 MB
18 - SageMaker Models Deployment.mp4
- 133.81 MB
3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner
19 - Project Overview.mp4
- 21.34 MB
20 - Simple Linear Regression Intuition.mp4
- 60.3 MB
21 - Least Sum of Squares.mp4
- 52.16 MB
22 - AWS SageMaker Linear Learner Overview.mp4
- 168.2 MB
23 - Coding Task #1A - Instantiate AWS SageMaker Notebook Instance (Method #1).mp4
- 195.42 MB
24 - Coding Task #1B - Using AWS SageMaker Studio (Method #2).mp4
- 88.43 MB
25 - Coding Task #2 - Import Key libraries and dataset.mp4
- 67.48 MB
26 - Coding Task #3 - Perform Exploratory Data Analysis.mp4
- 144.04 MB
27 - Coding Task #4 - Create Training and Testing Dataset.mp4
- 91.98 MB
28 - Coding Task #5 - Train a Linear Regression Model in SkLearn.mp4
- 74.29 MB
29 - Coding Task #6 - Evaluate Trained Model Performance.mp4
- 62.73 MB
30 - Coding Task #7 - Train a Linear Learner Model in AWS SageMaker.mp4
- 483.72 MB
31 - Coding Task #8 - Deploy Model & invoke endpoint in SageMaker.mp4
- 125.12 MB
4 - Project #2 - Medical Insurance Premium Prediction
32 - Project Overview and Introduction.mp4
- 11.44 MB
33 - Multiple Linear Regression Intuition.mp4
- 20.82 MB
34 - Regression Metrics and KPIs - RMSE, MSE, MAE, MAPE.mp4
- 83.4 MB
35 - Regression Metrics and KPIs - R2 and Adjusted R2.mp4
- 83.01 MB
36 - Coding Task #1 & #2 - Import Dataset and Key Libraries.mp4
- 136.07 MB
37 - Coding Task #3 - Perform Exploratory Data Analysis.mp4
- 158.23 MB
38 - Coding Task #4 - Perform Data Visualization.mp4
- 112.73 MB
39 - Coding Task #5 - Create Training and Testing Datasets.mp4
- 75.77 MB
40 - Coding Task #6 - Train a Machine Learning Model Locally.mp4
- 57.93 MB
41 - Coding Task #7 - Train a Linear Learner Model in AWS SageMaker.mp4
- 344.44 MB
42 - Coding Task #8 - Deploy Trained Model and Invoke Endpoint.mp4
- 111.42 MB
43 - Artificial Neural Networks for Regression Tasks.mp4
- 70.23 MB
44 - Activation Functions - Sigmoid, RELU and Tanh.mp4
- 20.03 MB
45 - Multilayer Perceptron Networks.mp4
- 19.64 MB
46 - How do Artificial Neural Networks Train.mp4
- 41.44 MB
47 - Gradient Descent Algorithm.mp4
- 105.63 MB
48 - Backpropagation Algorithm.mp4
- 22.82 MB
49 - Coding Task #9 - Train Artificial Neural Networks for Regression Tasks.mp4
- 250.2 MB
5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)
50 - Introduction to Case Study.mp4
- 73.16 MB
51 - Basics - What is the difference between Bias & Variance.mp4
- 66.42 MB
52 - Basics - L1 & L2 Regularization - Part #1.mp4
- 32.15 MB
53 - Basics - L1 & L2 Regularization - Part #2.mp4
- 16.24 MB
54 - Introduction to XGBoost (Extreme Gradient Boosting) algorithm.mp4
- 34.99 MB
55 - What is Boosting.mp4
- 46.54 MB
56 - Decision Trees and Ensemble Learning.mp4
- 35.98 MB
57 - Gradient Boosted Trees - Deep Dive - Part #1.mp4
- 179.8 MB
58 - Gradient Boosted Trees - Deep Dive - Part #2.mp4
- 76.52 MB
59 - AWS SageMaker XGBoost Algorithm.mp4
- 55.81 MB
60 - Project Introduction and Notebook Instance Instantiation.mp4
- 105.05 MB
61 - Coding Task #1 #2 #3 - Load Dataset_Libraries and Perform Data Exploration.mp4
- 225.33 MB
62 - Coding Task #4 - Merge and Manipulate DataFrame Using Pandas.mp4
- 73.62 MB
63 - Coding Task #5 - Explore Merged Datasets.mp4
- 63.18 MB
64 - Coding Task #6 #7 - Visualize Dataset.mp4
- 205.17 MB
65 - Coding Task #8 - Prepare the Data To Perform Training.mp4
- 33.6 MB
66 - Coding Task #9 - Train XGBoost Locally.mp4
- 84.81 MB
67 - Coding Task #10 - Train XGBoost Using SageMaker.mp4
- 175.95 MB
68 - Coding Task #11 - Deploy XGBoost endpoint and Make Predictions.mp4
- 69.15 MB
69 - Coding Task #12 - Perform Hyperparameters Tuning.mp4
- 166.42 MB
70 - Coding Task #13 - Retrain the Model Using best (optimized) Hyperparameters.mp4
- 97.57 MB
6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)
71 - Introduction and Project Overview.mp4
- 98.53 MB
72 - Principal Component Analysis (PCA) Intuition.mp4
- 111.75 MB
73 - XGBoost for Classification Tasks (Review Lecture).mp4
- 54.98 MB
74 - Confusion Matrix.mp4
- 53.47 MB
75 - Precision, Recall, and F1-Score.mp4
- 207.3 MB
76 - Area Under Curve (AUC) and Receiver Operating Characteristics (ROC) Metrics.mp4
- 41.98 MB
77 - Overfitting and Under fitting Models.mp4
- 20.23 MB
78 - Coding Task #1 - SageMaker Studio Notebook Setup.mp4
- 57.87 MB
79 - Coding Task #2 & #3 - Import Data_Libraries & Perform Exploratory data analysis.mp4
- 89.06 MB
80 - Coding Task #4 & #5 - Visualize Datasets & Prepare Training_Testing Data.mp4
- 90.84 MB
81 - Coding Task #6 - Train & Test XGboost and Perform Grid Search (Local Mode).mp4
- 229.01 MB
82 - Coding Task #7 - Train a PCA Model in AWS SageMaker.mp4
- 155.61 MB
83 - Coding Task #8 - Deploy Trained PCA Model Endpoint & Envoke endpoint.mp4
- 93.77 MB
84 - Coding Task #9 - Train XGBoost (SageMaker Built-in) to do Classification Tasks.mp4
- 115.47 MB
85 - Coding Task #10 - Deploy Endpoint, Make Inference @ Test Model.mp4
- 83.07 MB
7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker
86 - Project Overview and Introduction.mp4
- 96.8 MB
87 - What are Convolutional Neural Networks and How do they Learn - Part #1.mp4
- 118.08 MB
88 - What are Convolutional Neural Networks and How do they Learn - Part #2.mp4
- 124.29 MB
89 - How to Improve CNNs Performance.mp4
- 13.27 MB
90 - Confusion Matrix.mp4
- 40.48 MB
91 - LeNet Network Architecture.mp4
- 85.72 MB
92 - Request AWS SageMaker Service Limit Increase.mp4
- 4.99 MB
93 - Coding Part #1 #2 - Import Images and Visualize Them.mp4
- 157.85 MB
94 - Coding #3 #4 - Upload Training_Testing Data to S3.mp4
- 56.04 MB
95 - Coding Task #5 - Build and Train CNNs.mp4
- 206.11 MB
96 - Coding Task #6 - Deploy Trained Model Using SageMaker.mp4
- 70.47 MB
SageMaker+Practical+Course+Package.zip
- 145.15 MB
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InfoHash:
1F373A4D53F676FC15686F8F75A02E9CD66D165A
Last updated:
Jan 25, 2022
File count:
97 B
File Category:
1
Tags:
sagemaker, practical, beginners, build, projects
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