All Topics
🤖Machine Learning
🎯

Supervised Learning

Master classification and regression algorithms from fundamentals to advanced techniques

Recommended for:📊Data Scientist🔬ML Researcher🤖LLM Engineer

Prerequisites

→Linear Algebra→Probability & Statistics→Python for ML
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Beginner

Beginner

Understand core supervised learning concepts

What to Learn

  • •Classification vs regression tasks
  • •Linear regression: intuition and math
  • •Logistic regression for classification
  • •Train/test split and cross-validation
  • •Evaluation metrics: accuracy, precision, recall, F1

Resources

  • 📚Coursera ML by Andrew Ng (weeks 1-3)
  • 📚Scikit-learn documentation tutorials
  • 📚Kaggle Learn: Intro to ML
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Intermediate

Intermediate

Apply diverse ML algorithms

What to Learn

  • •Decision trees and Random Forests
  • •Support Vector Machines (SVM)
  • •Gradient Boosting (XGBoost, LightGBM)
  • •Feature engineering techniques
  • •Hyperparameter tuning strategies

Resources

  • 📚Hands-On Machine Learning (Géron) Ch 1-7
  • 📚XGBoost documentation
  • 📚Kaggle competitions for practice
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Advanced

Advanced

Deep supervised learning expertise

What to Learn

  • •Ensemble methods and stacking
  • •AutoML and neural architecture search
  • •Handling imbalanced datasets
  • •Calibration and uncertainty quantification
  • •Model interpretability (SHAP, LIME)

Resources

  • 📚Elements of Statistical Learning
  • 📚Interpretable ML book by Molnar
  • 📚Research papers on specific algorithms