Why it worksThe exact resources to finish your project
No filler theory. What you need, when you need it.
Real decisions explained. You see what the options were, which one was chosen, and why.
Phase checkpoints. You know if you're on track before moving to the next stage.
Common mistakes documented. Every phase includes the mistakes a junior would make — reading them saves you hours.
Just-in-time knowledge. The exact resources at the exact moment, no filler, no hours of video to get through first.
Reusable base code. Each project's pipeline is adaptable — swap out the data and you have the structure for your own case.
Real industrial problems. Mining, health, retail, energy, government — not made-up academic exercises.
How you progress
How you progressYou build judgment. You don't just follow instructions.
Each project is organized as a sequence of decisions.
1
You enter a real project. Mining, health, retail, energy, government. Problems that exist in the industry, not invented academic exercises. See projects →
2
You follow the development as a series of decisions. Each stage has a brief, a decision map, and blocks that make you think before you see the solution.
3
At each point you see: options, choice, and why.
Key decision: Regression or classification? Why? What happens if you get it wrong?
Common mistake: what a junior would do vs. someone with real experience on this problem.
4
You apply that reasoning to your own project. The goal isn't to replicate the code — it's to take away the thinking process. That's what lets you finish.
Reference projects
ProjectsReal problems you can solve
Filter by sector or explore all of them.
Business
Markowitz with ML for Investment Portfolios
Asset price prediction with LSTM
Portfolio optimization with Markowitz
Jan 2026
1.7k views
Business
Sales Forecasting, Segmentation, and Anomaly Detection
Full forecasting and clustering pipeline for retail
Anomaly detection in commercial transactions
Mar 2026
2.5k views
Education
Student Dropout Prediction — Step by Step
Predictive model with SHAP to identify dropout causes
Full pipeline: research gap, baseline, iteration, and deployment
Mar 2026
3.4k views
Industry
Cement — Compressive Strength Prediction
Regression on cement composition vs. compressive strength
Prediction across different curing times
Mar 2026
1.2k views
Agriculture
Grocery Price Prediction
Regression on local prices + nutritional optimization
Lowest-cost diet with nutritional constraints
Mar 2026
2.4k views
What people who used it say
TestimonialsProjects finished. Theses defended.
★★★★★
"What helped me most was seeing how the metric choice gets justified. That's exactly what they ask you about most in the defense."
RV
Rafael V.
Thesis · Mining Engineering · Peru
★★★★★
"I'd been stuck for weeks. The key decision blocks were exactly what I needed to stop second-guessing myself."
LC
Laura C.
Thesis · Data Science · MX
★★★★★
"I needed to show my manager that the model was useful. The evaluation section with business context was key for that conversation."
MC
Marcos C.
Analyst · Operations · Chile
★★★★☆
"I understood why XGBoost isn't always the answer. I switched models and improved results without blind tuning."
What people who used it say
TestimonialsProjects finished. Theses defended.
"What helped me most was seeing how the metric choice gets justified. That's exactly what they ask you about most in the defense."
Rafael V.
Thesis · Mining Engineering · Peru
"I'd been stuck for weeks. The key decision blocks were exactly what I needed to stop second-guessing myself."
Laura C.
Thesis · Data Science · MX
"I needed to show my manager that the model was useful. The evaluation section with business context was key for that conversation."
Marcos C.
Analyst · Operations · Chile
"I understood why XGBoost isn't always the answer. I switched models and improved results without blind tuning."
Diana P.
ML Engineer · Portfolio · CO