PYTHON

SKLEARN

JUPYTER

ML

PANDAS

COLAB

🧠

MLSTARTERKIT

From Zero to 3 ML Portfolio Projects in One Week

Learn by Building Real Models with Real Data

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3 Real Projects. Real Data.

Portfolio-ready models you'll build from scratch

🎵

Classification

Spotify Hit Predictor

Predict whether a song will be a hit using audio features like tempo, energy, and danceability

🎬

Regression

Movie Rating Predictor

Predict ratings using budget, genre, and cast data with linear and ensemble models

🏀

Random Forests

NBA Position Classifier

Classify player positions from performance stats using ensemble learning and feature importance

📚What You'll Learn

📊

Collect and clean real-world datasets from APIs and CSV sourcesData Collection

🤖

Train sklearn models end-to-end with cross-validation pipelinesModel Training

🎯

Build binary and multi-class classifiers for prediction tasksClassification

📈

Predict continuous outcomes with linear and ensemble regressionRegression

🌲

Master ensemble learning and feature importance analysisRandom Forests

📋

Evaluate models with accuracy, confusion matrices, and ROC curvesModel Evaluation

📑Complete Table of Contents

📖MODULE 0: PYTHON REFRESHER

✓NumPy, Pandas, and Matplotlib crash course
✓Data loading, cleaning, and EDA
✓Setting up your Colab environment

📖MODULE 1: PROJECT 1 — SPOTIFY HIT PREDICTOR

✓Understanding classification problems
✓Feature engineering from audio data
✓Logistic Regression + Decision Trees
✓Evaluating with accuracy and F1-score

📖MODULE 2: PROJECT 2 — MOVIE RATING PREDICTOR

✓Framing regression problems
✓Handling missing data and outliers
✓Linear Regression + Ridge/Lasso
✓Evaluating with RMSE and R²

📖MODULE 3: PROJECT 3 — NBA POSITION CLASSIFIER

✓Multi-class classification
✓Random Forests and feature importance
✓Hyperparameter tuning with GridSearchCV
✓Deploying your model to a Colab shareable

🎯Who It's For

💻

CS Students

who need portfolio projects to show recruiters and stand out in a competitive job market

🛠️

Self-Taught Coders

who know Python basics and want to apply ML to real-world problems with guided, hands-on projects

📺

Tutorial Watchers

who are stuck in tutorial hell and need structured, project-based learning to finally ship something real

💼

Job Seekers

who want data science and ML experience on their resume and need concrete projects to talk about in interviews

❓Frequently Asked Questions

Ready to Build Your ML Portfolio?

3 real projects. Real data. Portfolio-ready in one week.