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Launch your career in Machine Learning. Learn the fundamentals of AWS ML tools to prepare for AWS Certified Machine Learning Specialty Certification
Instructor: Whizlabs Instructor
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Recommended experience
Beginner level
Some recommended knowledge and experience for AWS Certified Machine Learning - Specialty Specialization is described below:
Minimum two year of hands-on experience in architecting, building or running ML/deep learning workloads on the AWS Cloud.
Experience in handling ML/deep learning frameworks.
Basic understanding and ability to express the purpose of the Machine learning algorithms.
Ability to perform basic hyperparameter optimization.
Ability to incorporate operational and deployment practices.
Recommended experience
Beginner level
Some recommended knowledge and experience for AWS Certified Machine Learning - Specialty Specialization is described below:
Minimum two year of hands-on experience in architecting, building or running ML/deep learning workloads on the AWS Cloud.
Experience in handling ML/deep learning frameworks.
Basic understanding and ability to express the purpose of the Machine learning algorithms.
Ability to perform basic hyperparameter optimization.
Ability to incorporate operational and deployment practices.
Fundamentals of Machine Learning Algorithms
Demonstrate AWS Services for Machine Learning.
Preparation for AWS Certified Machine Learning Specialty Certification
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Gain Skills to understand the fundamentals of Machine Learning.
Learn working with various AWS Services necessary for Machine Learning.
Hands on Experience working with AWS Management Console.
Prepare for AWS Certified Machine Learning Specialty Certification
Applied Learning Project
Learners will e provided with Lab Demonstrations based on the topics discussed in the courses that are part of this specialization. They will also have the opportunity to practice labs on their AWS Console for hands on demonstrations. However, These hands on labs are optional to be performed by learners.
Analyze various data gathering techniques
Analyze techniques to handle missing data
Implement feature extraction and feature selection with Principal Component Analysis and Variance Thresholds
Analyze and visualize data for machine learning
Demonstrate the implementation of Kinesis Data Streams
Examine AWS Glue service with creation of crawler and transform job
Analyze Modeling Concepts and train Machine Learning Models
Examine performance of machine learning models
Implement automatic model tuning by training a model
Determine algorithm concepts in ML
Design Regression algorithms and Classification based algorithms
Examine Reinforcement learning algorithms and Forecasting algorithms
Design machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance
Implement appropriate machine learning services and features for a given problem
Develop machine learning solutions with lab demonstration
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10 Weeks
There is no pre-requisite for the AWS Certified Machine Learning - Specialty Specialization. However, Some recommended knowledge and experience for AWS Certified Machine Learning - Specialty Specialization is described below:
Minimum two year of hands-on experience in architecting, building or running ML/deep learning workloads on the AWS Cloud.
Experience in handling ML/deep learning frameworks.
Basic understanding and ability to express the purpose of the Machine learning algorithms.
Ability to perform basic hyperparameter optimization.
Ability to incorporate operational and deployment practices.
No, there is no specific order requirement to follow for this specialization. All the courses are Independent.
User will get a better understanding of the AI, Chatbots, Data Analytics and Machine Learning concepts etc., and he/she will be ready for the real time AWS Certified Machine Learning Specialty Certification.
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.
This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.
Financial aid available,