AWS Certified Machine Learning – Specialty

Welcome to EDUNX, where we offer an expertly crafted training program for the AWS Certified Machine Learning – Specialty certification. This course is designed for professionals seeking to demonstrate their expertise in designing, implementing, and maintaining machine learning (ML) solutions on the AWS Cloud.

Our program equips participants with in-depth knowledge and practical skills to solve complex ML challenges using AWS services. Whether you’re a seasoned data scientist or an aspiring ML engineer, our course ensures you’re ready to excel in the certification exam and your career.

This course is ideal for:
Data Scientists and ML Engineers: Professionals aiming to enhance their expertise in AWS-based machine learning solutions.
Developers and IT Professionals: Those interested in gaining specialized knowledge in AWS ML services.
Cloud Architects: Individuals looking to integrate ML solutions into AWS-based cloud architectures.
Students and Fresh Graduates: Aspiring professionals who want to kickstart their careers in machine learning and cloud technologies.

Course Content

Introduction to Machine Learning on AWS

Overview of AWS ML services and tools.
Fundamentals of ML and its applications.

Exploratory Data Analysis (EDA)

Building and Training ML Models

Deploying and Managing Models

Advanced Topics

Security and Compliance

Exam Preparation and Strategies

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Price:
Original price was: $1,349.00.Current price is: $1,249.00.
Instructor:rakesh@edunx.org
Lessons:14
Students:0
Level:Intermediate

Requirements

Audience

Course Content

Introduction to Machine Learning on AWS

Overview of AWS ML services and tools.

Fundamentals of ML and its applications.

Exploratory Data Analysis (EDA)

Data preprocessing and feature engineering.

Tools and techniques for data visualization and analysis.

Building and Training ML Models

Using Amazon SageMaker for model building and training.

Selecting appropriate ML algorithms for specific use cases.

Deploying and Managing Models

Deploying models to production with Amazon SageMaker.

Monitoring and optimizing deployed models for performance.

Advanced Topics

Deep learning with AWS services (e.g., TensorFlow, PyTorch).

Reinforcement learning and custom algorithm implementation.

Security and Compliance

Implementing security best practices for ML workflows.

Data privacy and compliance considerations in AWS ML projects.

Exam Preparation and Strategies

Key exam domains: Data Engineering, Exploratory Data Analysis, Modeling, and Operations.

Mock tests, detailed explanations, and real-world scenarios for exam readiness.

Course Includes:

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AWS Certified Machine Learning – Specialty

Requirements

Audience

Course Content

Introduction to Machine Learning on AWS

Overview of AWS ML services and tools.

Fundamentals of ML and its applications.

Exploratory Data Analysis (EDA)

Data preprocessing and feature engineering.

Tools and techniques for data visualization and analysis.

Building and Training ML Models

Using Amazon SageMaker for model building and training.

Selecting appropriate ML algorithms for specific use cases.

Deploying and Managing Models

Deploying models to production with Amazon SageMaker.

Monitoring and optimizing deployed models for performance.

Advanced Topics

Deep learning with AWS services (e.g., TensorFlow, PyTorch).

Reinforcement learning and custom algorithm implementation.

Security and Compliance

Implementing security best practices for ML workflows.

Data privacy and compliance considerations in AWS ML projects.

Exam Preparation and Strategies

Key exam domains: Data Engineering, Exploratory Data Analysis, Modeling, and Operations.

Mock tests, detailed explanations, and real-world scenarios for exam readiness.

Student Ratings & Reviews

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