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The Complete AWS Machine Learning Course. Learn to Code
Introduction to Python
00. Introduction (4:47)
01. Intro To Python (5:46)
02. Variables (19:34)
03. Type Conversion Examples (10:21)
04. Operators (7:21)
05. Operators Examples (22:09)
06. Collections (8:39)
07. Lists (11:55)
08. Multidimensional List Examples (8:22)
09. Tuples Examples (8:51)
10. Dictionaries Examples (14:41)
11. Ranges Examples (8:46)
12. Conditionals (6:58)
13. If Statement Examples (10:32)
14. If Statement Variants Examples (11:35)
15. Loops (7:17)
16. While Loops Examples (11:47)
17. For Loops Examples (11:35)
18. Functions (8:04)
19. Functions Examples (9:33)
20. Parameters And Return Values Examples (14:08)
21. Classes and Objects (11:30)
22. Classes Example (13:28)
23. Objects Examples (10:10)
24. Inheritance Examples (17:43)
25. Static Members Example (11:20)
26. Summary and Outro (4:23)
Python_Language_Basics
Intro to Python Slides
Intro to Sagemaker
00. Course Intro (3:15)
01. Intro To Sagemaker (20:39)
02. Creating An AWS Account (8:23)
03. Exploring Sagemaker Interface (7:50)
04. Creating Sagemaker Files (9:53)
05. Summary And Outro (3:10)
06. Project Files
Data and S3
00. Course Intro (2:17)
01. Intro To S3 (12:25)
02. Storing Data In S3 (14:47)
03. Storing Downloaded Data In S3 (17:49)
04. Fetching Data From S3 (8:07)
05. Summary And Outro (3:46)
06. Project Files
Building a Linear Learner in SageMaker
00. Course Intro (4:54)
01. Intro To MNIST (8:23)
02. Getting And Formatting Dataset Part 1 (13:45)
03. Getting And Formatting Dataset Part 2 (19:24)
04. Intro To Linear Learner (3:59)
05. Building And Training The Model (10:28)
06. Deploying The Model (13:36)
07. Deleting The Endpoint (2:19)
08. Summary And Outro (4:04)
09. Project Files
Using the Debugger
00. Course Intro (4:22)
01. Intro To The Debugger (11:39)
02. Project Setup (9:17)
03. Building The Estimator (9:28)
04. Examining Results (16:01)
05. Examing Tensor Performance (14:32)
06. Summary And Outro (4:21)
07. Project Files
SciKit-Learn
00. Course Intro (4:09)
01. Intro To Scikit-Learn (4:03)
02. Exploring The Dataset (3:29)
03. Project Setup (6:10)
04. Importing And Uploading The Dataset (12:38)
05. Creating And Training The Model (10:59)
06. Testing The Model (9:59)
07. Summary And Outro (3:57)
08. Project Files
XGBoost
00. Course Intro (5:36)
01. Intro To XGBoost (5:27)
02. Intro To MNIST (8:15)
03. Project Setup (6:12)
04. Fetching, Formatting, And Uploading The Dataset (16:29)
05. Training The XGBoost Model (8:14)
06. Deploying And Hosting The Model (7:20)
07. Testing And Validating The Model (12:50)
08. Project Overview (8:22)
09. Summary And Outro (3:21)
10. Project Files
Tensorflow Tuning
00. Course Intro (5:15)
01. Intro To Tensorflow (6:19)
02. Intro To Mnist (9:10)
03. Project Setup (8:37)
04. Examining MNIST And Utils Scripts (22:10)
05. Downloading, Formatting, And Uploading Dataset (7:48)
06. Building The Model Containers (9:44)
07. Launching The Tuning Job (5:34)
08. Summary And Outro (3:21)
09. Resources
Project - Image Classification
00. Course Intro (4:22)
01. Project Setup (6:46)
02. Getting And Uploading The Dataset (9:04)
03. Creating The Training Job (13:44)
04. Creating The Inference Model (20:48)
05. Deploying And Hosting The Model (5:55)
06. Realtime Inference (9:50)
07. Summary And Outro (3:55)
08. AWS - Image Classification with Caltech 256
09. Resource Files
Project - Movie Genre Prediction
00. Course Intro (4:54)
01. Project Setup (7:01)
02. Fetching And Manipulating The Movies Dataset (21:33)
03. Fetching And Manipulating The Vocab Dataset (25:48)
04. Creating And Training The Model (13:12)
05. Evaluating Model Performance (10:52)
06. Deploying And Predicting With The Model (7:01)
07. Summary And Outro (3:55)
08. AWS - Movie Genre Predictions
09. Resource Files
08. AWS - Image Classification with Caltech 256
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