AI & Machine Learning
Machine Learning Engineering
Train, evaluate and improve supervised and unsupervised ML models.
Overview
Go from data to working predictive models. You learn the core machine learning algorithms, how to engineer good features, and how to train, evaluate and tune models the way industry teams do.
Course objectives
- Apply supervised and unsupervised algorithms
- Engineer and select useful features
- Train, evaluate and tune models
- Build end-to-end ML pipelines
Who it's for
- Python users ready to learn machine learning properly
- Data analysts moving into predictive work
- Engineers who want to understand ML models end to end
Prerequisites
- Python basics, including Pandas
- Basic statistics is helpful
- Or complete Python for AI & Machine Learning first
Syllabus outline
- Supervised vs unsupervised learning
- The ML project workflow
- Bias, variance and overfitting
Lab and practice approach
Each module ends with a lab on a real dataset. You finish with a capstone predictive analytics project you can show in your portfolio.
Why attend this course?
- Machine learning skills are in high demand
- Teaches the workflow used by real ML teams
- Portfolio-ready capstone project
After completing this course
Roles you can aim for
- Machine Learning Engineer
- Data Scientist
- Predictive Analytics Specialist
What you will be able to do
- Regression and classification
- Model evaluation and tuning
- Feature engineering
Assessments and capstone project
No vendor exam is required. You are assessed through assignments, quizzes and knowledge checks, technical assessments and a final capstone project.
- Assessment
- Assignments, quizzes and technical assessments
- Final project
- Capstone project reviewed by your instructor
- Vendor exam
- Not required
Why learn with OpenSprig
- Live, instructor-led online sessions you can ask questions in
- Practice on real systems in every class
- Exam-objective-aligned syllabus with mock tests
- Course community group for help between classes
- Weekend-friendly 2–3 hour classes
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