Zero to Machine Learning: Jump-Start Your Data-Driven Journey

Overview

Discover how to initiate data-driven projects using AWS analytics and ML capabilities, focusing on low-code solutions and best practices for organizations with limited resources.

Syllabus

• Introduction to Machine Learning and Data Analytics
Overview of Machine Learning and its Importance
Introduction to Data-Driven Decision Making
• Basics of AWS for Data and ML Initiatives
Introduction to AWS Services Related to ML
Overview of AWS Analytics Services
Setting Up AWS Environment
• Fundamental Concepts of Machine Learning
Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
Key Algorithms and Their Use Cases
Understanding Model Training and Evaluation
• Low-Code Machine Learning Solutions
Introduction to Low-Code Tools on AWS
Basics of Amazon SageMaker and SageMaker Canvas
Building ML Models Using Low-Code Solutions
• Collecting and Preparing Data
Data Collection Techniques and Sources
Data Cleaning and Transformation Best Practices
Using AWS Glue for Data Preparation
• Deploying and Operationalizing Machine Learning Models
Model Deployment Options on AWS
Introduction to AWS Lambda and API Gateway for Deployments
Monitoring and Maintaining Model Performance
• Analytics and Visualization
Using AWS QuickSight for Data Visualization
Building Dashboards to Support Business Decisions
• Best Practices for Resource-Constrained Organizations
Cost Management and Optimization on AWS
Leveraging Open Source and Community Resources for ML
Effective Project Management and Team Collaboration
• Case Studies and Real-World Applications
Case Studies from Various Industries
Lessons Learned from Successful Data-Driven Projects
• Capstone Project
Define and Execute a Data-Driven Project Using AWS
Present Findings and Insights
• Conclusion and Further Learning
Recap of Key Concepts
Recommended Resources for Continued Learning
Q&A and Wrap-up Session

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Subjects

Data Science