This machine learning fundamentals tutorial series teaches you to build production-ready ML models from scratch. Whether you’re predicting customer churn or employee attrition, you’ll learn practical machine learning skills using Python and real datasets—no theoretical math, just code that works.
Who This Series Is For
You’re a data professional who:
- Knows Python and SQL basics
- Wants to build real ML models, not toy examples
- Prefers practical code over theoretical math
- Needs solutions that work in production
What You’ll Build
Throughout this series, you’ll build complete, production-ready models for:
Customer Churn
Predict which customers will cancel subscriptions, identify retention opportunities, and prioritize at-risk accounts.
Employee Attrition
Forecast which employees are likely to quit, flag retention risks, and optimize HR interventions.
Tutorial Series
01
ML Fundamentals: Stop Overthinking, Start Building
Learn what ML actually is (curve fitting with extra steps) and build your first spam classifier. Understand supervised learning, train/test splits, and how it all applies to churn prediction.
What You’ll Build: Spam classifier → Churn prediction foundation
Key Concepts: Binary classification, training/testing, Naive Bayes
Time: ~30 minutes | Code: ~50 lines
02
Data Prep: Where ML Projects Actually Live or Die
The 80% of ML work nobody talks about. Handle missing values, scale features, avoid data leakage, and build real data pipelines for customer and employee datasets.
What You’ll Build: Complete data pipeline for churn prediction
Key Concepts: Missing data, feature scaling, train/test/validation splits, data leakage
Time: ~45 minutes | Code: ~80 lines
03
Classification Models: Pick the Right Tool
Compare logistic regression, decision trees, random forests, and gradient boosting. Learn when to use each one and how they perform on real churn data.
What You’ll Build: Customer churn model comparing 4 algorithms
Key Concepts: Algorithm selection, interpretability vs performance, ensemble methods
Time: ~60 minutes | Code: ~120 lines
04
Feature Engineering: The Part That Actually Matters
Learn where ML projects actually succeed or fail. Engineer recency, frequency, monetary, and behavioral features from raw transaction data. Build better models through better features.
What You’ll Build: Customer lifetime value predictor with engineered features
Key Concepts: Feature creation, RFM analysis, temporal patterns, feature importance
Time: ~60 minutes | Code: ~150 lines
05
Model Evaluation: Beyond Accuracy
Why accuracy is a trap. Master precision, recall, F1 score, ROC-AUC, and precision-recall curves. Learn to adjust decision thresholds and calculate actual business impact. Choose the right metrics for your problem.
What You’ll Build: Complete evaluation framework with business metrics
Key Concepts: Confusion matrix, precision vs recall, ROC curves, threshold tuning, business impact
Time: ~60 minutes | Code: ~140 lines
06
Hyperparameter Tuning: When It Matters
Learn when hyperparameter tuning actually helps and when it’s a waste of time. Master GridSearchCV and RandomizedSearchCV. Optimize Random Forest parameters and measure whether tuning was worth the computational cost.
What You’ll Build: Optimized Random Forest with performance comparison
Key Concepts: GridSearch, RandomSearch, cross-validation, cost-benefit analysis
Time: ~75 minutes | Code: ~180 lines
07
Model Deployment: Getting to Production
Deploy ML models to production with FastAPI. Build REST APIs, handle real-time predictions, manage model versioning, and containerize with Docker. This is where ML becomes real.
What You’ll Build: Production-ready churn prediction API
Key Concepts: FastAPI, model serialization, Docker, deployment, logging
Time: ~90 minutes | Code: ~200 lines
08
Model Monitoring: Keeping Models Working
Coming soon — Monitor model performance in production. Detect data drift, track prediction quality, and know when to retrain.
What You’ll Build: Complete monitoring dashboard with alerts
Key Concepts: Performance monitoring, data drift detection, retraining triggers
Prerequisites
To get the most from this series, you should have:
- Python basics: Variables, functions, loops, pandas
- SQL knowledge: SELECT, JOIN, WHERE (we’ll integrate with databases)
- Data familiarity: Comfortable working with dataframes
- No ML experience required: We start from zero
Download All Code
All tutorial code, datasets, and Jupyter notebooks are available on GitHub:
GitHub Repository: github.com/randalscottking/ml-tutorial-series