Preparing Data for Feature Engineering and Machine Learning
Bad data kills ML models before they start—and most teams spend 80% of their time fixing it. This course cuts through the noise, teaching you the exact data preparation and feature engineering workflows that separate production-ready models from academic exercises. You’ll move from raw datasets to engineered features in under 3.5 hours.
AIU.ac Verdict: Ideal for data engineers, junior ML engineers, and analytics professionals who need to bridge the gap between messy real-world data and model-ready datasets. The course is practical and hands-on, though it assumes basic Python familiarity—complete beginners may need supplementary resources first.
What This Course Covers
You’ll work through the full data preparation pipeline: exploratory data analysis (EDA) to uncover quality issues, handling missing values and outliers, scaling and normalisation techniques, and categorical encoding strategies. The course emphasises why each step matters, not just the mechanics—you’ll understand when to apply which technique and why it impacts model performance.
The feature engineering section covers domain-driven feature creation, polynomial and interaction features, and dimensionality reduction. Janani uses real datasets and practical labs so you’re building muscle memory for the decisions you’ll make daily: which features to engineer, how to validate them, and how to avoid data leakage. By the end, you’ll have a repeatable workflow for any ML project.
Who Is This Course For?
Ideal for:
- Data Engineers: Building ETL pipelines and data infrastructure for ML teams. You’ll learn the feature engineering expectations your ML colleagues have.
- Junior ML Engineers & Data Scientists: Transitioning from notebooks to production. This course teaches the unglamorous but critical work that determines whether your models actually work.
- Analytics Professionals Upskilling to ML: You understand data quality; this course shows you how to translate that into ML-ready feature sets and avoid common pitfalls.
May not suit:
- Complete Programming Beginners: The course assumes Python literacy. Start with a Python fundamentals course first.
- Advanced ML Researchers: If you’re already deep in feature selection theory and automated ML pipelines, this foundational course won’t challenge you.
Frequently Asked Questions
How long does Preparing Data for Feature Engineering and Machine Learning take?
3 hours 17 minutes of video content. Most learners complete it in 1–2 sittings, though hands-on labs may extend that depending on your pace and experimentation.
Do I need prior machine learning experience?
No—the course is designed for people new to ML. You do need basic Python (variables, loops, libraries like pandas). If you’re uncomfortable with Python syntax, take a Python fundamentals course first.
Will I get hands-on practice?
Yes. Pluralsight includes sandboxed labs where you’ll apply techniques to real datasets. You’re not just watching; you’re coding.
Is this enough to prepare data for production models?
It’s a strong foundation covering 80% of day-to-day data prep work. For domain-specific challenges (time series, NLP, image data), you may need supplementary learning—but the principles here transfer directly.
Course by Janani Ravi on Pluralsight. Duration: 3h 17m. Last verified by AIU.ac: March 2026.
