Hands-On Labs: MLS-CO1: AWS Certified Machine Learning Specialty- Based On Real World Case Studies by Specialist IP

Author:Specialist, IP
Language: eng
Format: epub, pdf
Published: 2022-08-14T00:00:00+00:00

Lab Diagram

Implementation Steps

Upload Data into S3.

Import all the necessary libraries.

Load the data from Amazon S3.

Clean, transform, analyze, and prepare the dataset.

Create and Train our model (XGBoost).

Create and train our model (Linear Learner).

Solution

Note: A lot of trial and error happens during the machine learning process. This is just part of the machine learning process. You will continuously create jobs that fail, and you will figure out the reasons why. You will realize that you do not have enough data, so you will have to get more. You will realize your data may not be in the right format. You might have accidentally left out a hyperparameter or used the wrong value for a hyperparameter. You will realize that your model is extremely inaccurate, so you must change something. You will realize that one algorithm works better than the other. But you need to remember that the machine learning process is a trial-and-error.

Step 1: Upload Data into S3

1. Log in to the AWS Console.

2. Go to Services and click on S3.

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