Best Free AI Courses from Amazon for 2025

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2025-02-12

Amazon is offering a range of AI courses for free, providing an excellent opportunity for anyone interested in advancing their knowledge in artificial intelligence. Below are the details of the courses, along with practical commands and codes to help you get started.

1. Generative AI Learning Plan

Duration: 11 Hours | Courses: 5

  • to Generative AI
  • Planning a Generative AI Project
  • Amazon Bedrock Getting Started
  • Foundations of Prompt Engineering
  • Building Generative AI Applications

Practical Command:

To set up a basic environment for Generative AI on AWS, use the following command to install the necessary SDK:

pip install boto3

2. Generative AI Learning Plan for Decision Makers

Duration: 3 Hours | Courses: 3

  • to Generative AI – Art of the Possible
  • Planning a Generative AI Project
  • Building a Generative AI-Ready Organization

Practical Command:

To list all available AWS services related to AI, use:

aws sagemaker list-notebook-instances

3. Foundation of Prompt Engineering (stand-alone)

Duration: 4 Hours

Practical Command:

To test a simple prompt engineering script in Python:

import openai
openai.api_key = 'your-api-key'
response = openai.Completion.create(engine="text-davinci-003", prompt="Translate English to French: Hello, how are you?")
print(response.choices[0].text.strip())

4. Low-Code Machine Learning on AWS

Practical Command:

To deploy a low-code ML model using AWS SageMaker:

aws sagemaker create-model --model-name MyModel --primary-container Image=382416733822.dkr.ecr.us-east-1.amazonaws.com/linear-learner:latest --execution-role-arn arn:aws:iam::123456789012:role/AmazonSageMaker-ExecutionRole

5. Building Language Models on AWS

Duration: 5h 30m

Practical Command:

To train a language model using SageMaker:

aws sagemaker create-training-job --training-job-name MyTrainingJob --algorithm-specification TrainingImage=382416733822.dkr.ecr.us-east-1.amazonaws.com/blazingtext:latest --role-arn arn:aws:iam::123456789012:role/AmazonSageMaker-ExecutionRole --input-data-config '{"ChannelName":"train","DataSource":{"S3DataSource":{"S3DataType":"S3Prefix","S3Uri":"s3://mybucket/train/","S3DataDistributionType":"FullyReplicated"}}}' --output-data-config S3OutputPath=s3://mybucket/output/ --resource-config InstanceType=ml.m4.xlarge,InstanceCount=1,VolumeSizeInGB=50 --stopping-condition MaxRuntimeInSeconds=1800

6. Amazon Transcribe Getting Started

Duration: 1h 30m

Practical Command:

To transcribe audio using Amazon Transcribe:

aws transcribe start-transcription-job --transcription-job-name MyTranscriptionJob --media MediaFileUri=s3://mybucket/audio.mp3 --language-code en-US --output-bucket-name mybucket

7. Machine Learning Learning Plan

Duration: 12h 13m | Total 10 courses inside

Practical Command:

To list all available SageMaker notebooks:

aws sagemaker list-notebook-instances

What Undercode Say

The field of AI and machine learning is rapidly evolving, and staying updated with the latest tools and technologies is crucial. Amazon’s free AI courses provide a comprehensive learning path for both beginners and advanced users. Here are some additional Linux and IT commands to enhance your AI and machine learning workflows:

1. Monitoring AWS Resources:

aws cloudwatch list-metrics --namespace AWS/SageMaker

2. Managing Docker Containers for AI Models:

docker run -it --rm tensorflow/tensorflow:latest-gpu python -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"

3. Automating Data Pipelines:

cronjob -e
*/5 * * * * /usr/bin/python3 /path/to/your/script.py

4. Securing AI Models:

aws iam create-role --role-name SageMakerRole --assume-role-policy-document file://trust.json

5. Optimizing Model Performance:

nvidia-smi

6. Data Preprocessing with Linux Commands:

awk -F, '{print $1, $2}' data.csv > processed_data.csv

7. Version Control for AI Projects:

git clone https://github.com/your-repo/ai-project.git

8. Automating Model Deployment:

ansible-playbook deploy_model.yml

9. Monitoring System Performance:

htop

10. Backup and Restore AI Models:

rsync -avz /path/to/model /backup/location/

By integrating these commands into your workflow, you can streamline your AI and machine learning projects, ensuring efficiency and security. For more detailed tutorials and resources, consider visiting the official AWS documentation and community forums.

Useful URLs:

By leveraging these resources and commands, you can significantly enhance your AI and machine learning capabilities, making you a more proficient and effective practitioner in the field.

References:

Hackers Feeds, Undercode AIFeatured Image

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