Python for Machine Learning

This hybrid course covers:

• what is machine learning, and what is AI?
• pipelines for data ingestion, training and testing: scikit-learn
• examples of classic algorithms: scikit-learn
  • principal component analysis
  • Ridge regression
  • Naive Bayes classifier
  • k-means clustering
• examples of (deep) neural networks: Keras
  • multi-layer perceptron for image classification
  • convolutional neural network for image classification
  • recurrent neural networks for sentiment analysis

When you complete this training you will

• have an understanding of the architecture and features of GPGPUs,
• be able to transfer data between the host and the GPGPU device,
• be able to do linear algebra computations on GPGPUs using scikit-cuda,
• be able to generate random numbers on a GPGPU using curand,
• be able to define your own kernels to run on GPGPUs,
• use numba to generate kernels to run on GPGPUs,
• run machine learning algorithms on GPGPUs,
• speed up data science tasks using Rapids.