Amazon’s Free AI Courses: A Comprehensive Guide for 2025

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Amazon is offering a range of AI courses for free, providing an excellent opportunity for anyone looking to upskill in artificial intelligence. Below are the details of the courses, along with practice-verified codes and commands related to the topics covered.

1. Generative AI Learning Plan

  • Duration: 11 Hours | Courses: 5
  • Topics Covered:
  • to Generative AI
  • Planning a Generative AI Project
  • Amazon Bedrock Getting Started
  • Foundations of Prompt Engineering
  • Building Generative AI Applications
  • URL: Generative AI Learning Plan

Practice Code:


<h1>Example of a simple Generative AI model using TensorFlow</h1>

import tensorflow as tf
from tensorflow.keras.layers import Dense, LSTM
model = tf.keras.Sequential([
LSTM(128, return_sequences=True),
Dense(64, activation='relu'),
Dense(10, activation='softmax')
])
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

2. Generative AI Learning Plan for Decision Makers

  • Duration: 3 Hours | Courses: 3
  • Topics Covered:
  • to Generative AI – Art of the Possible
  • Planning a Generative AI Project
  • Building a Generative AI-Ready Organization
  • URL: Generative AI for Decision Makers

Practice Code:


<h1>Example of a decision-making algorithm</h1>

def decision_making_algorithm(data):
if data['risk'] > 0.5:
return "High Risk - Avoid"
else:
return "Low Risk - Proceed"

3. Foundation of Prompt Engineering (Stand-alone)

Practice Code:


<h1>Example of prompt engineering</h1>

prompt = "Translate the following English text to French: 'Hello, how are you?'"
response = model.generate(prompt)
print(response)

4. Low-Code Machine Learning on AWS

Practice Code:


<h1>Example of low-code ML using AWS Sagemaker</h1>

import sagemaker
from sagemaker import get_execution_role
role = get_execution_role()
sagemaker_session = sagemaker.Session()

5. Building Language Models on AWS

Practice Code:


<h1>Example of building a language model using AWS Sagemaker</h1>

from sagemaker.huggingface import HuggingFace
estimator = HuggingFace(entry_point='train.py', role=role, transformers_version='4.6', pytorch_version='1.7')
estimator.fit({'train': 's3://my-bucket/train'})

6. Amazon Transcribe Getting Started

Practice Code:


<h1>Example of using Amazon Transcribe</h1>

import boto3
transcribe = boto3.client('transcribe')
job_name = "my-transcription-job"
job_uri = "s3://my-bucket/my-audio-file.wav"
transcribe.start_transcription_job(TranscriptionJobName=job_name, Media={'MediaFileUri': job_uri}, MediaFormat='wav', LanguageCode='en-US')

7. Machine Learning Learning Plan

Practice Code:


<h1>Example of a simple ML model using Scikit-learn</h1>

from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()
model.fit(X_train, y_train)
predictions = model.predict(X_test)

What Undercode Say

The courses offered by Amazon provide a robust foundation for anyone looking to delve into the world of AI and machine learning. From generative AI to low-code machine learning on AWS, these courses cover a wide range of topics that are essential for both beginners and experienced professionals. The practical examples provided above give a glimpse into how these technologies can be implemented in real-world scenarios. Whether you’re looking to build language models, transcribe audio, or simply understand the basics of machine learning, these courses are a valuable resource. Additionally, the integration of AWS services like SageMaker and Transcribe offers a hands-on approach to learning, making it easier to apply these skills in a professional setting. For further reading and more advanced topics, consider exploring the official AWS documentation and additional resources available online.

Useful Commands:

  • Linux: `aws configure` to set up AWS CLI.
  • Windows: `aws s3 cp s3://my-bucket/my-file.txt C:\local\path` to copy files from S3.
  • General: `pip install boto3` to install the AWS SDK for Python.

These courses and resources are a stepping stone to mastering AI and machine learning, and with consistent practice, you can achieve significant milestones in your career.

References:

Hackers Feeds, Undercode AIFeatured Image

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