Real-World Machine Learning Project with XGBoost and NVIDIA GPU

Learn to build a complete machine learning workflow using real-world NYC taxi data to predict tipping patterns, leveraging GPU acceleration with XGBoost CUDA and cuDF Pandas for handling massive datasets efficiently. Master professional data science techniques including data cleaning, missing value handling, anomaly detection, and memory optimization while working with 38 million rows of taxi trip data. Discover how to set up and utilize NVIDIA GPUs both locally and through Google Colab's free T4 GPU, install RAPIDS for GPU-accelerated data processing, and solve common memory limitations and runtime crashes using RMM (RAPIDS Memory Manager). Practice essential data preprocessing steps such as detecting and replacing missing values, investigating ambiguous column names, shuffling and splitting data for training and testing, and handling negative charges and unrealistic transaction values. Implement XGBoost model training and evaluation on GPU, troubleshoot common errors like DataFrame dtype issues and GPU memory exhaustion, and apply data optimization techniques to improve model performance. Explore advanced topics including feature importance analysis, hyperparameter tuning, date feature extraction, and K-fold cross-validation while developing the analytical mindset needed to approach real-world data science problems systematically rather than through guesswork.