AWS Machine Learning Certification Mastery for Cloud Professionals
This course is designed for cloud professionals at different proficiency levels to achieve certification in AWS Machine Learning. We focus on exam preparation, leveraging mnemonics, tools, and AI tutors based on well-known cloud experts to help students memorize content and excel in their certification exam.
What You'll Learn
- Memory Tools & Mnemonics to Master AWS Concepts
- Comprehensive AWS Machine Learning Exam Preparation
- Hands-on Labs for Real-World Application
- Personalized Guidance from AI Tutors
- In-Depth Study of AWS ML Tools like SageMaker, Glue, Rekognition, and Polly
AI Mentor Inspiration
Jassy Cloudman
Based on Andy Jassy, this AI tutor provides high-level strategic insights on AWS cloud and machine learning architecture.
Werner Expertson
Based on Werner Vogels, this AI tutor helps students understand complex technical concepts in AWS ML services.
Detailed Schedule
Week 1
Certification Exam Review and AWS ML Overview
Review the structure of the AWS Machine Learning certification exam and gain an understanding of key AWS ML services.
- Topics:
- AWS ML Exam Structure
- SageMaker Overview
- AWS Rekognition
- Polly
Week 1
Machine Learning Fundamentals in AWS
Dive into the basics of machine learning and its application in AWS.
- Topics:
- Supervised Learning
- Unsupervised Learning
- AWS SageMaker for ML Training
Week 2
Data Engineering and Preparation
Learn how to clean, normalize, and prepare data using AWS Glue and Lake Formation.
- Topics:
- Data Cleaning
- AWS Glue for ETL
- AWS Lake Formation
Week 3
Building and Training ML Models with SageMaker
Learn to build, train, and deploy models using SageMaker.
- Topics:
- Training Models
- Deploying Models on AWS
- Using SageMaker Notebooks
Week 4
AI Services for Text and Image Processing
Explore Amazon Rekognition, Polly, and other AI services for speech, text, and image processing.
- Topics:
- Amazon Rekognition
- Amazon Polly
- Amazon Textract
Week 5
Optimizing Machine Learning Models
Learn about hyperparameter tuning and model optimization.
- Topics:
- Hyperparameter Tuning
- Model Optimization
- SageMaker Debugger
Week 6
Security and Compliance for Machine Learning
Understand the importance of security, compliance, and governance in AWS ML.
- Topics:
- IAM for ML
- Encryption
- Compliance in ML Workflows
Week 7
Final Project: Building a Full AWS ML Solution
Apply your knowledge to build an end-to-end machine learning pipeline using AWS.
- Topics:
- AWS ML Pipeline
- Building Full-Scale ML Solutions
- SageMaker, Glue, Polly Integration
Week 8
Exam Preparation and Review
Focus on exam-style questions, review of key concepts, and final practice exams.
- Topics:
- Exam Practice
- Key AWS ML Concepts
- Review of Mnemonics
Student Experiences
Word Cloud
Learning with Cohorts and AI
Learn Better with Cohorts
Collaborate with peers to enhance knowledge retention through shared study techniques.
AI-Powered Learning
AI tutors will offer personalized support, answering questions and providing real-time feedback.
Frequently Asked Questions
Who should take this course?
Cloud professionals at different skill levels who are aiming to get certified in AWS Machine Learning and transition into AI/ML roles.
What is the main focus of the course?
The course is focused on helping participants pass the AWS Machine Learning certification exam. It provides a deep dive into AWS ML tools and services with exam-specific preparation techniques.
What are the prerequisites?
Basic understanding of cloud computing concepts and experience with AWS is recommended, but not mandatory.
How is the course structured?
The course includes live sessions, certification review, hands-on labs, personalized AI tutor support, and mock exams.
Will I be prepared for the AWS Machine Learning certification exam?
Yes, the course is specifically designed to prepare students for the AWS Machine Learning certification exam. It includes exam simulations, mnemonics for quick recall, and hands-on labs for real-world experience.
Course Details
- 8 weeks
- Cohort-based learning
- Start anytime
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