AWS Certified Machine Learning – Specialty
Learn how to use AWS services like SageMaker, Glue, and Forecast to create, train, and implement machine learning models. Through practical, real-world projects, this course gets you ready for the AWS ML Specialty certification.
Level
Advanced
Duration
8 weeks
Placement Client
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About AWS Certified Machine Learning – Specialty
Learn how to use AWS to create data-driven, intelligent solutions. Get practical experience with Glue, Forecast, and SageMaker for real-world machine learning projects. Develop your skills in data preparation, model training, and large-scale deployment. Prepare yourself completely to obtain the AWS Machine Learning – Specialty certification.
Training Plan
About trainer
Working professional who is carrying more then 10 years of industry experience.
Decks & Updated Content
Access to updated presentation decks shared during live training sessions.
e-Book
E-book provided by TechPratham. All rights reserved.
Assignments & MCQs
Module-wise assignments and MCQs provided for practice.
Video Recording
Daily Session would be recorded and shared to the candidate.
Projects
Live projects will be provided for hands-on practice.
Resume Building
Expert-guided resume building with industry-focused content support.
Interview Preparation
Comprehensive interview preparation with real-time scenario practice.
AWS Certified Machine Learning – Specialty
Course Curriculum
Data Engineering for ML
Prepare and manage data pipelines, repositories, and ingestion using AWS services to support ML workflows.
Data Preparation & Feature Engineering
Clean, preprocess, and engineer features to improve model training and performance.
Exploratory Data Analysis (EDA)
Explore and understand dataset distributions, relationships, outliers, and visualize data to inform modeling decisions.
Model Selection & Algorithm Fundamentals
Understand different ML algorithm types and when to apply them.
Model Training & Hyperparameter Tuning
Train ML models using AWS tools, optimize hyperparameters, and avoid overfitting/underfitting.
Performance Evaluation & Metrics
Evaluate model performance using relevant metrics and statistical tests.
AWS Courses
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Who Should Take AWS Certified Machine Learning – Specialty
IT Professionals
Non-IT Career Switchers
Fresh Graduates
Working Professionals
Ops/Administrators/HR
Developers
BA/QA Engineers
Cloud / Infra
IT Professionals
Non-IT Career Switchers
Fresh Graduates
Key Projects
<h1>AWS Certified Machine Learning – Specialty</h1>
Fraud Detection System
Using Amazon Kinesis, AWS Glue, and SageMaker, a real-time fraud detection model was created. It was then deployed via API Gateway and watched over by CloudWatch to identify irregularities at scale.
Recommendation Engine
created a product recommendation system using SageMaker and Amazon Personalize, integrated it using API Gateway + Lambda, and monitored its performance using QuickSight dashboards.
Demand Forecasting
Using SageMaker DeepAR and Amazon Forecast, a sales forecasting model was developed. RDS/S3 data was processed, and QuickSight was used to provide insights for inventory optimization.
Placement Process
Live Class with Industry Use Cases
- Interactive learning
- Real-world examples
- Expert guidance
- Doubt resolution
Daily Assignment & Weekly Assessments
- Hands-on practice
- Concept reinforcement
- Weekly evaluation
- Timely feedback
Project Testing & Deployment
- Project development
- Testing practice
- Deployment exposure
- Portfolio creation
Interview Preparation (Mock, PD, Alumni Session)
- Mock interviews
- Personal development
- Alumni guidance
- Confidence building
Resume Building
- Resume crafting
- Industry-focused format
- Skills highlighting
- Professional presentation
Placement
- Job assistance
- Interview support
- Career guidance
- Opportunity access
Knowledge Center
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Course FAQs
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Industry-Recognized Certification
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