ML Fundamentals Bootcamp: Housing Price Prediction
Master the core concepts of machine learning by building a housing price prediction model from scratch. This bootcamp covers supervised and unsupervised learning, model evaluation, and practical implementation with scikit-learn and Python — all through a hands-on housing dataset scenario.
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Lesson Overview
Lesson 1: What is Machine Learning?
Learn what machine learning is, its types, when to use it, and meet the housing price dataset we'll use throughout the bootcamp.
Lesson 2: Your First Dataset — Exploring Housing Data
Load the California Housing dataset, perform exploratory data analysis with pandas, visualize distributions and correlations, and identify key patterns.
Lesson 3: Linear Regression — Predicting Prices
Understand the math behind linear regression, implement it with scikit-learn to predict California housing prices, and evaluate with R-squared and RMSE.
Lesson 4: Feature Engineering & Preprocessing
Learn how to scale features, encode categorical variables, handle missing values, create derived features, and build reusable preprocessing pipelines.
Lesson 5: Classification — Is This a Luxury Home?
Convert the housing problem to binary classification, learn logistic regression and decision trees, and evaluate with confusion matrix, precision, recall, and F1.
Lesson 6: Model Evaluation & Cross-Validation
Master proper model evaluation with k-fold cross-validation, understand the bias-variance tradeoff, and learn to compare models fairly.
Lesson 7: Ensemble Methods — Random Forest & Gradient Boosting
Harness the power of ensemble learning with Random Forest and Gradient Boosting to improve housing price predictions and discover which features matter most.
Lesson 8: Unsupervised Learning — Neighborhood Clustering
Use K-Means clustering and PCA to segment California neighborhoods into meaningful groups based on housing characteristics.
Lesson 9: Hyperparameter Tuning & Pipelines
Build end-to-end sklearn Pipelines with ColumnTransformer and tune hyperparameters with GridSearchCV and RandomizedSearchCV.
Lesson 10: Handling Real-World Data Issues
Detect and handle outliers, multicollinearity, class imbalance, and data leakage in production datasets.
Lesson 11: Model Deployment Basics
Save trained models with joblib, build a FastAPI prediction endpoint, and serve housing price predictions via REST API.
Lesson 12: ML Interview Q&A & Capstone
Review common ML interview questions, consolidate all 12 lessons, and present the housing price project as a portfolio-ready capstone.