Accelerated Data Analytics: Machine Learning with GPU-Accelerated Pandas and Scikit-learn
- Introduction to GPU-accelerated data analytics and the use of RAPIDS cuDF and cuML libraries for machine learning tasks.
- Overview of the Meteonet dataset, including the meaning of each column.
- Accelerating basic machine learning techniques such as classification, regression, and clustering with cuDF and cuML.
- Preprocessing steps for time series data and training ML models efficiently.
- Understanding algorithm performance and evaluation metrics for each ML task.
- Example of using the Random Forest Classifier in cuML for weather classification and wind direction prediction.
- Example of using linear regression in cuML to predict temperature and humidity from weather station data.
- Clustering weather conditions using the K-Means algorithm and the Elbow Method to determine the optimal number of clusters.