Explore Machine Learning Methods
and Algorithms with Python
Python is one of the dominant languages in the area of machine learning. This training will provide an introduction to machine learning methodology as well as some machine learning algorithms.
Programme
Subjects and Python modules that will be covered:
- 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
Learning objectives
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.