About this course
In today’s data-driven world, Machine Learning (ML) is at the heart of innovation across industries. This beginner-friendly course introduces you to the core terminology, processes, and practices of machine learning, with a focus on helping you understand how ML is applied in real-world scenarios—including in cloud ecosystems like AWS.
Designed for newcomers to the field, this course offers step-by-step guidance through the machine learning pipeline—from framing ML problems and preparing data sets to understanding feature engineering, model training, and evaluation techniques. You’ll explore how ML models align with business goals and learn how to analyze predictions to extract insights and drive decisions.
Whether you're pursuing AWS certification, exploring AI/ML for the first time, or looking to build foundational knowledge for data science roles, this course offers a practical, up-to-date entry point into machine learning.
Note: Some exercises may involve using AWS services, which can incur additional costs.
Learning Objectives
• Understand basic machine learning terminology and workflow
• Frame machine learning problems effectively
• Prepare, clean, and analyze data sets
• Apply feature engineering techniques and create data visualizations
• Train, evaluate, and interpret machine learning models
• Align ML models with real business goals and use cases
Target Audience
• Beginners interested in AI and machine learning
• Professionals exploring data science or AWS cloud ML tools
• Students preparing for introductory ML certifications
Prerequisites
• Basic understanding of data concepts and interest in machine learning
• Familiarity with using web-based tools and spreadsheets
• Optional: AWS account for hands-on exercises
Designed for newcomers to the field, this course offers step-by-step guidance through the machine learning pipeline—from framing ML problems and preparing data sets to understanding feature engineering, model training, and evaluation techniques. You’ll explore how ML models align with business goals and learn how to analyze predictions to extract insights and drive decisions.
Whether you're pursuing AWS certification, exploring AI/ML for the first time, or looking to build foundational knowledge for data science roles, this course offers a practical, up-to-date entry point into machine learning.
Note: Some exercises may involve using AWS services, which can incur additional costs.
Learning Objectives
• Understand basic machine learning terminology and workflow
• Frame machine learning problems effectively
• Prepare, clean, and analyze data sets
• Apply feature engineering techniques and create data visualizations
• Train, evaluate, and interpret machine learning models
• Align ML models with real business goals and use cases
Target Audience
• Beginners interested in AI and machine learning
• Professionals exploring data science or AWS cloud ML tools
• Students preparing for introductory ML certifications
Prerequisites
• Basic understanding of data concepts and interest in machine learning
• Familiarity with using web-based tools and spreadsheets
• Optional: AWS account for hands-on exercises
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