Explore various machine learning models through a comprehensive video series covering topics from K-means clustering to neural networks. Learn to implement machine learning techniques using Python, Julia, and Keras. Dive into concepts such as simple linear regression, QR decomposition, and the Jacobian matrix. Gain practical skills in sharing Python models, handling class imbalance in neural networks, and applying the Gram-Schmidt process. Access accompanying files on GitHub to enhance your learning experience. Includes a lecture on Real Analysis by Eva Sincich.
Overview
Syllabus
Machine learning.
K means clustering using python.
Sharing your Python machine learning model.
Least squares method for simple linear regression.
Ordinary least squares tutorial using Julia.
Real Analysis - Eva Sincich - Lecture 01.
Gram Schmidt process for QR decomposition using Python.
Basics of the Jacobian and its use in a neural network using Python.
Training a neural network in Keras with class imbalance.
Taught by
Dr Juan Klopper
