Learn Cloud Computing: AWS Certified Machine Learning Specialty
Last updated Jun 2024
Course Overview
The AWS Certified Machine Learning Specialty certification is designed to validate your expertise in building, training, tuning, and deploying machine learning (ML) models on AWS. The course encompasses a broad range of ML techniques and AWS services to ensure comprehensive learning and application.
Prerequisites
- Experience:
Minimum of 1-2 years of experience developing, architecting, or running
ML/deep learning workloads on AWS.
- Knowledge:
In-depth understanding of the AWS ML services and how to apply them to the
ML lifecycle.
Key Topics Covered
- Data
Engineering and Feature Engineering
- Data
Analysis and Visualization
- Modeling
Techniques and Algorithms
- Model
Training, Hyperparameter Tuning, and Optimization
- Machine
Learning Operations (MLOps)
- Deployment
and Monitoring of ML Models
Benefits of Certification
- Validates
proficiency in ML and AI on AWS.
- Opens
up new job opportunities and potentially higher salaries.
- Recognized
credential by employers globally.
- Enhances
your ability to contribute to AI and ML projects within your organization.
Benefits of Taking This Course
- Recognizes your skills in ML and AI on AWS.
- Enhances your career prospects in data science and ML engineering.
- Validates your ability to design and implement scalable, cost-optimized ML solutions.
- Provides access to the AWS Certified community and resources.
Included in this course
Perfectly curated Tests designed to help candidate prepare for the Certification Exam.
Topic-wise Content Distribution
Free Test1
Free Test1
Practice Tests5
1. Practice Test 01
2. Practice Test 02
3. Practice Test 03
4. Practice Test 04
5. Practice Test 05
Section Tests6
1. Core ML Concepts
2. Exploratory Data Analysis
3. Data Engineering
4. Modeling
5. Machine Learning Implementation and Operations
6. Additional Questions
Final Test1
Final Test
