Machine Learning Model Using AWS SageMaker Canvas Overview

The Machine Learning Model Using AWS SageMaker Canvas course provides a comprehensive introduction to building and deploying machine learning models using AWS SageMaker Canvas. Participants will explore the fundamentals of machine learning, gain an understanding of AWS services, and delve into the SageMaker Canvas interface. The course features hands-on projects, including banknote authentication, spam SMS detection, customer churn prediction, and wine quality prediction, to provide practical experience in model creation and evaluation. By the end of this course, learners will have a solid foundation in using SageMaker Canvas for real-world applications and be prepared for further exploration in the field of machine learning.

Learning Outcomes

• Understand the fundamental concepts of machine learning.
• Gain knowledge of AWS and its relevant services for machine learning.
• Familiarise yourself with the AWS SageMaker Canvas interface.
• Set up AWS SageMaker Canvas for machine learning projects.
• Implement a banknote authentication model using SageMaker Canvas.
• Develop a spam SMS detection model with SageMaker Canvas.
• Create a customer churn prediction model using SageMaker Canvas.
• Build a wine quality prediction model with SageMaker Canvas.
• Explore and utilise additional features within SageMaker Canvas.
• Apply best practices for deploying and managing machine learning models on AWS.

Who Is This Course For

This course targets individuals keen on machine learning and data science, especially those eager to utilise AWS SageMaker Canvas for model building and deployment. It suits beginners with a basic grasp of machine learning concepts who want practical experience with AWS tools. Additionally, data analysts, business analysts, and IT professionals aiming to boost their skills in machine learning and cloud-based solutions will find this course exceptionally valuable.

Eligibility Requirements

Participants should have a basic understanding of machine learning concepts and familiarity with cloud computing, especially AWS services. No prior experience with SageMaker Canvas is required, but a foundational knowledge of data science principles and AWS fundamentals is beneficial.

Entry Requirements

• Age Requirement: Applicants must be aged 16 or above, allowing both young learners and adults to engage in this educational pursuit.
• Academic Background: There are no specific educational prerequisites, opening the door to individuals from diverse academic histories.
• Language Proficiency: A good command of the English language is essential for comprehension and engagement with the course materials.
• Numeracy Skills: Basic numeracy skills are required to effectively understand and work with course-related information. 

Why Choose Us

• Affordable, engaging & high-quality e-learning study materials;
• Tutorial videos/materials from the industry-leading experts;
• Study in a user-friendly, advanced online learning platform;
• Efficient exam systems for the assessment and instant result;
• The UK & internationally recognised accredited
• Access to course content on mobile, tablet or desktop from anywhere, anytime;
• The benefit of career advancement opportunities;
• 24/7 student support via email.
  

Career Path

Completing this course actively paves the way for careers in machine learning and data science. Graduates can confidently pursue roles such as machine learning engineer, data scientist, and AI specialist. They will focus on developing models, managing data-driven projects, and leveraging cloud-based solutions. As the demand for these skills continues to grow, this course not only provides a solid foundation but also significantly enhances career advancement opportunities.