AWS Certified Machine Learning Engineer – Associate Intended

Name: AWS Certified Machine Learning Engineer – Associate Intended
Rating: 4.0 (3 reviews)

Master Machine Learning on AWS: Build, Train, and Deploy Scalable Models in the Cloud

Created byRabia Atika, Muhammad Asif Nadeem

Last updated 12/2024

English

English [Auto],

What you'll learn

Master end-to-end machine learning workflows, including data preparation, model training, and deployment using AWS services.
Gain proficiency in AWS tools like SageMaker, Rekognition, and Comprehend to build, train, and deploy scalable ML solutions.
Learn hyperparameter tuning, feature engineering, and performance evaluation to optimize machine learning models for real-world applications.
Apply security, scalability, and cost optimization best practices while designing machine learning pipelines and deploying solutions on AWS.

Course content

4 sections • 9 lectures • 32m total length

lecture2 AWS ML Services and their applications3:07
Discover how AWS machine learning and its core services, including SageMaker and Lambda, drive scalable, flexible solutions across data preprocessing, training, deployment, and monitoring, with real-world industry use cases.
Introduction1:47
Master machine learning concepts with AWS tools to design, deploy, and optimize AI solutions, and prepare for associate exam across data engineering, modeling, and machine learning implementation operations.

lecture 3 Data Engineering4:05
Master data engineering for machine learning by collecting, storing, and cleaning structured, unstructured, and semi-structured data using AWS tools such as S3, RDS, DynamoDB, and Glue.
Lecture 5 AWS Tools for data Engineering3:55
Explore Amazon SageMaker Data Wrangler, Redshift, and Athena to design robust data engineering architectures, import data from S3, Redshift, and DynamoDB, and automate model tuning with SageMaker Autopilot.
Lecture 4 Feature Engineering3:52
Master feature engineering by selecting and extracting meaningful features, handling missing data, and applying scaling and normalization to boost model performance.

Lecture 6 EDA3:45
Explore exploratory data analysis (EDA) to examine data sets before modeling, uncover patterns, distributions, and anomalies, ensure data quality, and guide feature engineering and algorithm choices.
Data Visualization tools3:45
Lecture 8 Statistical analysis and data insights3:54
Explore data distribution with histograms, box plots, PDFs, and CDFs; identify patterns, correlations, and anomalies, and summarize with mean, median, variance, and standard deviation to inform decisions and model performance.

Requirements

A basic understanding of machine learning concepts, Python programming, and familiarity with AWS services is recommended for this course.

Description

This course is designed for professionals and aspiring individuals aiming to specialize in the field of machine learning (ML) with a focus on AWS technologies. It equips learners with the knowledge and skills to design, deploy, and optimize machine learning solutions on the AWS platform.

Key Highlights:

Explore the fundamentals of machine learning and deep learning concepts.

Learn how to build, train, and deploy ML models using AWS services such as SageMaker, Lambda, and more.

Gain expertise in data engineering, feature engineering, and model optimization tailored for scalable solutions.

Understand the ethical and practical considerations of AI implementation in cloud environments.

Who Should Enroll?

This course is ideal for:

Data scientists, software engineers, and developers looking to integrate ML into their workflows.

IT professionals seeking to enhance their cloud computing skills with a focus on AI.

Students and tech enthusiasts aspiring to gain certification and excel in AWS ML engineering roles.

Prepare to master the intersection of artificial intelligence and cloud computing while advancing your career in the fast-evolving world of machine learning! This certification opens doors to high-demand roles in AI and ML engineering. Employers worldwide value AWS-certified professionals for their proven expertise in building innovative, scalable machine learning solutions.

AWS Certified Machine Learning Engineer – Associate Intended

What you'll learn

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Course content

Introduction2 lectures • 5min

Data Enginereing3 lectures • 12min

Section 3 Exploratory Data Analysis3 lectures • 11min

Section 4 Machine learning models1 lecture • 4min

Requirements

Description

Who this course is for: