Menu
Sign In Search Podcasts Charts People & Topics Add Podcast API Pricing
Podcast Image

CyberSecurity Summary

AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide: The definitive guide to passing the MLS-C01 exam on the very first

23 Dec 2025

Description

Focusing on practical applications of machine learning (ML) within the Amazon Web Services ecosystem. The content systematically covers the exam syllabus, starting with ML fundamentals like modeling pipelines, supervised and unsupervised learning, and data splitting strategies to prevent overfitting and underfitting. It then details various AWS services for AI/ML, including Amazon Rekognition for image/video analysis, Amazon Polly for text-to-speech, Amazon Transcribe for speech-to-text, and Amazon Comprehend for natural language processing (NLP), alongside storage solutions like Amazon S3, RDS, and Redshift. The guide also explains data preparation and transformation techniques, such as handling missing values, outliers, and unbalanced datasets, and explores different ML algorithms (e.g., linear regression, XGBoost, K-means) as well as their evaluation and optimization through metrics like precision, recall, and hyperparameter tuning using Amazon SageMaker.You can listen and download our episodes for free on more than 10 different platforms:https://linktr.ee/cyber_security_summaryGet the Book now from Amazon:https://www.amazon.com/Certified-Machine-Learning-Specialty-Certification/dp/1800569009?&linkCode=ll1&tag=cvthunderx-20&linkId=ec5d3390e2fb431864ff90c4f68df62c&language=en_US&ref_=as_li_ss_tlDiscover our free courses in tech and cybersecurity, Start learning today:https://linktr.ee/cybercode_academy

Audio
Featured in this Episode

No persons identified in this episode.

Transcription

This episode hasn't been transcribed yet

Help us prioritize this episode for transcription by upvoting it.

0 upvotes
🗳️ Sign in to Upvote

Popular episodes get transcribed faster

Comments

There are no comments yet.

Please log in to write the first comment.