Xây Dựng Mô Hình Machine Learning

ML Model Builder: create AI models for real-world problems! Full ML workflow: (1) Problem definition (predict house prices? classify images? recommend products?), (2) Data collection (datasets: Kaggle, UCI, scraping), (3) Data prep (cleaning, feature engineering, split train/test), (4) Model selection (linear regression, decision trees, neural networks, SVM), (5) Training (fit model to data), (6) Evaluation (accuracy, precision, recall, F1 score), (7) Deployment (API, web app, mobile). Popular libraries: scikit-learn (traditional ML), TensorFlow/PyTorch (deep learning), pandas (data manipulation), matplotlib (visualization).

Learn algorithms: supervised (labeled data - classification, regression), unsupervised (clustering, dimensionality reduction), reinforcement learning (AlphaGo strategy). Avoid pitfalls: overfitting (model memorizes training data, fails new data), data leakage (test info sneaks into training), biased data (garbage in = garbage out), wrong metrics (99% accuracy meaningless if 99% one class!). Real applications: spam filters, fraud detection, medical diagnosis, autonomous driving, recommendation systems.

AutoML tools: Google AutoML, H2O.ai automate process. Phù hợp data science students, Python programmers, AI enthusiasts!