From ML Fundamentals to Practice - Building Predictive Models that Learn from Data
Series Overview
This series is a practical educational content with 4 chapters that teaches Supervised Learning progressively from the basics.
Supervised Learning is a fundamental machine learning technique that learns from labeled data and makes predictions on unseen data. From the two main tasks of regression (numerical prediction) and classification (category prediction) to state-of-the-art ensemble methods, you'll master practical skills used in real-world applications.
Features:
- ✅ Balance of Theory and Practice: Learn both mathematical background and implementation code
- ✅ Implementation Focused: Over 40 executable Python code examples
- ✅ Latest Techniques: XGBoost, LightGBM, CatBoost and other techniques used in practice
- ✅ Practical Projects: Complete implementation of housing price prediction and customer churn prediction
- ✅ Kaggle Preparation: Master techniques usable in competitions
Total Learning Time: 80-100 minutes (including code execution and exercises)
How to Learn
Recommended Learning Path
🎯 Complete Mastery Course (All Chapters Recommended)
Target: ML beginners, those wanting systematic learning
Path: Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4
Duration: 80-100 minutes
Outcomes: Master from regression/classification basics to latest ensemble methods, complete 2 practical projects
⚡ Fast Track Course (Practice Focused)
Target: Those with basic knowledge, wanting to strengthen practical skills
Path: Chapter 3 (Ensemble Methods) → Chapter 4 (Practical Projects)
Duration: 50-60 minutes
Outcomes: Master XGBoost/LightGBM/CatBoost, ready for Kaggle
🔍 Focused Learning
Target: Those wanting to learn specific topics
- Regression Only: Chapter 1 (20-25 minutes)
- Classification Only: Chapter 2 (25-30 minutes)
- Ensemble Only: Chapter 3 (25-30 minutes)
- Practice Only: Chapter 4 (30 minutes)
Chapter Details
Chapter 1: Regression Fundamentals
Learning Content
- Linear Regression Theory and Implementation
- Mathematical Understanding of Least Squares
- Gradient Descent Implementation
- Polynomial Regression
- Regularization (Ridge, Lasso, Elastic Net)
- Evaluation on Real Data (R², RMSE, MAE)
Chapter 2: Classification Fundamentals
Learning Content
- Logistic Regression Theory
- Sigmoid Function and Probability Interpretation
- Decision Tree Mechanisms
- k-NN (k-Nearest Neighbors)
- Support Vector Machines (SVM)
- Evaluation Metrics (Accuracy, Recall, F1 Score, Confusion Matrix)
- ROC Curve and AUC
Chapter 3: Ensemble Methods
Learning Content
- Bagging Principles
- Random Forest Implementation and Feature Importance
- Boosting Principles
- Gradient Boosting Implementation
- XGBoost Practice
- LightGBM Practice
- CatBoost Practice
- Ensemble Method Comparison
- Kaggle Usage
Chapter 4: Practical Projects
Learning Content
Project 1: Housing Price Prediction (Regression)
- Data Loading and Exploratory Analysis
- Feature Engineering
- Model Selection and Evaluation
- Hyperparameter Tuning
Project 2: Customer Churn Prediction (Classification)
- Data Preprocessing
- Imbalanced Data Handling
- Model Comparison
- Business Impact Analysis
Overall Learning Outcomes
Upon completing this series, you will acquire the following skills and knowledge:
Knowledge Level (Understanding)
- ✅ Can explain the difference between regression and classification
- ✅ Understand the mathematical background of linear and logistic regression
- ✅ Understand the mechanisms of decision trees, SVM, and k-NN
- ✅ Can explain the difference between Bagging and Boosting
- ✅ Understand the characteristics of XGBoost, LightGBM, and CatBoost
- ✅ Can explain the necessity and methods of regularization
- ✅ Can select appropriate evaluation metrics
Practical Skills (Doing)
- ✅ Can implement linear regression from scratch with NumPy
- ✅ Can build regression/classification models with scikit-learn
- ✅ Can master XGBoost/LightGBM/CatBoost
- ✅ Can perform data preprocessing and feature engineering
- ✅ Can execute hyperparameter tuning
- ✅ Can evaluate model performance from multiple perspectives
- ✅ Can handle imbalanced data
Application Ability (Applying)
- ✅ Can select appropriate algorithms for new problems
- ✅ Can detect and address overfitting
- ✅ Can formulate business problems as ML problems
- ✅ Can participate in Kaggle competitions
- ✅ Can build practical predictive models for real-world use
Frequently Asked Questions (FAQ)
Q1: Can I learn without ML experience?
A: Yes. By learning from Chapter 1 sequentially, you can understand progressively from the basics. Knowing Python basics (variables, functions, lists) is sufficient.
Q2: I'm not good at math - is that okay?
A: High school-level math (calculus, linear algebra basics) is helpful. We supplement with intuitive explanations and code implementations beyond just formulas.
Q3: Which chapter should I start from?
A: Beginners from Chapter 1, those with ML experience from Chapter 3 (Ensemble Methods), and those strengthening practical skills from Chapter 4.
Q4: What environment is needed?
A: Python 3.7+, NumPy, pandas, scikit-learn, XGBoost, LightGBM, CatBoost, matplotlib. Using Google Colab eliminates environment setup.
Q5: Can I learn Kaggle-applicable techniques?
A: Yes. Chapter 3 covers XGBoost/LightGBM/CatBoost, and Chapter 4 teaches feature engineering and hyperparameter tuning.
Q6: Will I reach a practical skill level?
A: You'll master basic-level practical tasks (building, evaluating, and tuning predictive models). For more advanced techniques (deep learning, time series analysis, etc.), please refer to other series.
Let's Get Started!
Are you ready? Start with Chapter 1 and begin your journey into the world of supervised learning!
Update History
- 2025-10-20: v1.0 Initial Release
Your supervised learning journey starts here!