Preparing Data for Machine Learning
Bad data kills ML models before they start—and most teams spend 80% of project time fixing it. This course cuts through the noise, teaching you the exact data preparation workflows that separate production-ready models from failed experiments. You’ll move from raw datasets to ML-ready pipelines in under 3.5 hours.
AIU.ac Verdict: Essential for anyone building ML systems who’s tired of debugging data quality issues downstream. Best suited to engineers and analysts stepping into ML roles; less valuable if you’re already deep in advanced feature engineering or working exclusively with pre-cleaned datasets.
What This Course Covers
You’ll cover the full data preparation lifecycle: exploratory data analysis (EDA) to spot anomalies, handling missing values and outliers, normalisation and scaling techniques, and categorical encoding strategies. The course walks you through real-world scenarios where small preparation decisions compound into model performance gains—think imbalanced datasets, feature leakage, and temporal data pitfalls.
Janani Ravi structures this around hands-on labs where you’ll wrangle messy datasets, validate data quality, and engineer features that actually matter. You’ll learn when to use standardisation vs. normalisation, how to detect and handle data drift, and practical tricks for documenting your preparation pipeline so others can reproduce your work.
Who Is This Course For?
Ideal for:
- Data engineers transitioning to ML: You know data pipelines; this bridges to ML-specific preparation concerns like feature scaling and train-test splitting.
- Junior data scientists and analysts: Foundational knowledge that prevents months of frustration debugging models built on poorly prepared data.
- Software engineers building ML features: Practical, no-nonsense approach to getting data production-ready without getting lost in statistical theory.
May not suit:
- Advanced ML researchers: If you’re already publishing on feature engineering or working with domain-specific data, this will feel too introductory.
- Analysts working only with pre-cleaned datasets: Limited ROI if your data arrives already validated; better suited to those handling raw, messy sources.
Frequently Asked Questions
How long does Preparing Data for Machine Learning take?
3 hours 24 minutes of video content. Most learners complete it in 1–2 sittings, though hands-on labs may extend that depending on your pace.
Do I need prior ML experience?
No. The course assumes basic Python familiarity and general data handling knowledge, but doesn’t require previous ML projects. Janani teaches concepts from first principles.
Will this course include hands-on labs?
Yes. Pluralsight’s sandbox environments let you practise data cleaning, transformation, and feature engineering on real datasets without local setup.
Is this course vendor-specific (e.g., TensorFlow, scikit-learn)?
The course teaches language-agnostic principles and demonstrates with Python libraries like pandas and scikit-learn—applicable across any ML framework.
Course by Janani Ravi on Pluralsight. Duration: 3h 24m. Last verified by AIU.ac: March 2026.
