Whizlabs
Exam Prep MLA-C01: AWS Machine Learning Engineer Assocaite Specialization
Whizlabs

Exam Prep MLA-C01: AWS Machine Learning Engineer Assocaite Specialization

Become Machine Learning Engineer. Masters in AWS Machine Learning Engineer Associate Certification

Whizlabs Instructor

Instructor: Whizlabs Instructor

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Get in-depth knowledge of a subject
Intermediate level

Recommended experience

5 months at 28 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
Get in-depth knowledge of a subject
Intermediate level

Recommended experience

5 months at 28 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers

What you'll learn

  • Learners will master data ingestion, transformation, model training, tuning, deployment, and monitoring using Amazon SageMaker and AWS ML services.

  • Gain hands-on experience in building and optimizing ML models for real-world applications like classification, forecasting, and recommendations.

  • Gain the skills needed to earn the AWS Certified Machine Learning – Associate (MLA-C01) certification.

Overview

What’s included

Shareable certificate

Add to your LinkedIn profile

Taught in English
Recently updated!

September 2025

26 practice exercises

Advance your subject-matter expertise

  • Learn in-demand skills from university and industry experts
  • Master a subject or tool with hands-on projects
  • Develop a deep understanding of key concepts
  • Earn a career certificate from Whizlabs

Specialization - 5 course series

What you'll learn

  • Explore the core concepts of Machine Learning and how it differs from AI and Deep Learning.

  • Introduce key AWS services and MLOps practices for managing the end-to-end ML lifecycle.

  • Explore how to build and evaluate classification and regression models using AWS ML services.

  • Differentiate between batch and real-time inferencing methods and identify suitable use cases for each.

Skills you'll gain

Machine Learning, AWS SageMaker, Applied Machine Learning, Regression Analysis, MLOps (Machine Learning Operations), Supervised Learning, Data Processing, Continuous Deployment, Amazon Web Services, Feature Engineering, Predictive Modeling, Data Cleansing, Unsupervised Learning, and Artificial Intelligence and Machine Learning (AI/ML)

What you'll learn

  • Apply data cleaning, transformation, and feature engineering techniques to prepare datasets for machine learning.

  • Recognize methods to detect and reduce bias in data preparation and securely manage PII using AWS tools like DataBrew.

  • Implement ETL workflows using AWS Glue, Glue Crawlers, and DataBrew for data preparation.

  • Process large-scale datasets using Apache Spark on Amazon EMR for machine learning workloads.

Skills you'll gain

Feature Engineering, Extract, Transform, Load, Data Integrity, Data Transformation, Apache Spark, Data Validation, Amazon Web Services, AWS SageMaker, Personally Identifiable Information, Machine Learning Methods, Responsible AI, Data Pipelines, Data Quality, and Data Cleansing

What you'll learn

  • Explore built-in algorithms in Amazon SageMaker such as Linear Learner, XGBoost, LightGBM, and k-NN for ML model development.

  • Configure key training parameters like epochs, batch size, and steps to train and evaluate ML models effectively.

  • Compare real-time and batch inference approaches to determine the best strategy for model deployment.

Skills you'll gain

Amazon Elastic Compute Cloud, Continuous Deployment, Continuous Integration, and Debugging

What you'll learn

  • Compare AWS storage options and select the appropriate solution for ML data management.

  • Explore the end-to-end capabilities of Amazon SageMaker for building and managing ML workflows.

  • Secure sensitive data using AWS KMS and Secrets Manager for encryption and credential management.

Skills you'll gain

Data Pipelines, AWS SageMaker, Amazon CloudWatch, AWS Kinesis, Data Security, Data Storage, Amazon S3, Cloud Security, Real Time Data, Encryption, Feature Engineering, MLOps (Machine Learning Operations), AWS Identity and Access Management (IAM), and Amazon Redshift

What you'll learn

  • Implement intelligent search and document extraction with Amazon Kendra and Textract.

  • Create personalized experiences and human review workflows using Personalize, A2I, and Mechanical Turk.

  • Leverage AWS AI services like Comprehend, Translate, Transcribe, and Polly for language and speech processing tasks.

  • Apply Amazon Rekognition and Amazon Lex to build intelligent image analysis and conversational AI solutions.

Skills you'll gain

Natural Language Processing, AI Personalization, Fraud detection, Data Processing, Text Mining, Image Analysis, Artificial Intelligence, Artificial Intelligence and Machine Learning (AI/ML), Amazon Web Services, Computer Vision, and Document Management

Earn a career certificate

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Instructor

Whizlabs Instructor
Whizlabs
132 Courses88,308 learners

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