Kaggle 30 Days of ML Learning - Beginner-Friendly Machine Learning Challenge

Learn machine learning fundamentals through a comprehensive tutorial series that follows Kaggle's structured 30-day learning challenge, progressing from Python basics to advanced ML concepts. Master essential Python programming skills including functions, conditionals, loops, data structures, and libraries before diving into machine learning topics. Explore data exploration techniques, build your first ML models, and understand crucial concepts like validation, overfitting, and random forests. Gain hands-on experience with Kaggle competitions by making actual submissions and learning to handle real-world data challenges such as missing values and categorical encoding. Develop proficiency with industry-standard tools including scikit-learn pipelines, cross-validation, and XGBoost while understanding critical concepts like data leakage. Advance to interpretable machine learning and explainable AI (XAI) techniques, working with feature importance analysis using ELI5, partial dependence plots, and SHAP (Shapley) values for model interpretation. Each tutorial builds upon previous concepts, providing a structured pathway from complete beginner to competent machine learning practitioner with practical Kaggle platform experience.

Syllabus

Kaggle 30 Days of ML - Day 1 - Becoming Kaggle Contributor - Learn Python ML in 30 Days
Kaggle 30 Days of ML - Day 2 - Python Basics - Learn Python ML in 30 Days
Kaggle 30 Days of ML - Day 3 - Python Functions & Help - Learn Python ML in 30 Days
Kaggle 30 Days of ML - Day 4 - Python Boolean, Conditionals, IF-ELSE - Learn Python ML in 30 Days
Kaggle 30 Days of ML - Day 5 - Python List, Tuples - Learn Python ML in 30 Days
Kaggle 30 Days of ML - Day 5 - Python Loops, List Comprehension - Learn Python ML in 30 Days
Kaggle 30 Days of ML - Day 6 - Python String, Dictionary - Learn Python ML in 30 Days
Kaggle 30 Days of ML - Day 7 - Python Libraries & Operator Overloading - Learn Python ML in 30 Days
Kaggle 30 Days of ML - Day 8 - Machine learning Intro, Data Exploration - Learn Python ML in 30 Days
Kaggle 30 Days of ML - Day 9 - Build first ML Model, Validation - Learn Python ML in 30 Days
Kaggle 30 Days of ML - Day 10 - Overfitting, Random Forest - Learn Python ML in 30 Days
Kaggle 30 Days of ML - Day 11 - Kaggle Competition Submission - Learn Python ML in 30 Days
Kaggle 30 Days of ML - Day 12 - Kaggle Missing Values, Encoding - Learn Python ML in 30 Days
Kaggle 30 Days of ML (Day 13) - Scikit-Learn Pipeline, CrossValidation - Learn Python ML in 30 Days
Kaggle 30 Days of ML (Day 14) - XGBoost, Data Leakage - Learn Python ML in 30 Days
Kaggle 30 Days of ML (Day 15) - Interpretable Machine Learning Use-cases
Kaggle 30 Days of ML (Day 16) - Feature Importance of Machine Learning with ELI5
Kaggle 30 Days of ML (Day 17) - Partial Dependence Plot - Interpretable Machine Learning - XAI
Kaggle 30 Days of ML (Day 18) - SHAP - Shapley Values - Interpretable Machine Learning - XAI
Kaggle 30 Days of ML (Day 19) - Understanding SHAP Summary Plot - Interpretable Machine Learning