Machine Learning for Analysts: A Practical Guide is a structured track of 6 lessons that build a complete, interview-ready understanding of the topic. Work through them in order, then use the quiz and flashcards in each lesson to revise.
What this course covers
- ML Fundamentals: An Analyst's Perspective - Analyst's ML Mindset: You don't need to build TensorFlow models from scratch.
- Regression Models Explained for Analysts - Simple Linear Regression: y = β₀ + β₁x + ε y = output, x = input, β₀ = intercept, β₁ = slope, ε = error Multiple Regression: y = β₀ + β₁x₁ + β₂x₂ + ...
- Classification Models Explained for Analysts - Confusion Matrix - Key Metric Formulas Predicted → Low Predicted → High Actual Positive ↑ High True Positive (TP) Correctly predicted fraud / default False Positive (FP)
- Clustering & Customer Segmentation Explained - K-Means Clustering Process 1.
- Model Evaluation & Selection: How to Choose the Right Model - Bias-Variance Trade-off Visual Scenario Bias Variance Training Acc Test Acc Symptom Fix High Bias (Underfitting) High Low Low Low Model too simple - misses patterns More
- When NOT to Use ML: The Pragmatic Analyst's Guide - Ch 7: ML for Analysts The Analyst's Most Important Skill: Knowing when NOT to use ML.