Explore supervised learning algorithms in this comprehensive lecture, the third in a four-part series by Dr. Abdelrahman Kotb. Delve into key concepts including model training evaluation, introduction to Python, and classification examples. Gain insights into linear regression, overfitting, and underfitting. Learn how to work with data, understand random state, and apply Python code to real-world problems. This lecture is part of PioPetro's Summer Training 2024, covering topics in Petroleum Engineering, Energy Transition, Geology, Petrophysics, and Machine Learning. Perfect for those looking to enhance their knowledge in these fields and apply supervised learning techniques to practical scenarios.
Overview
Syllabus
Introduction
Agenda
Lecture 2 Recap
Supervised Learning
Model Training Evaluation
Introduction to Python
Classification Example
Python Code
Data
Random State
Linear Regression
Overfitting and Underfitting
Taught by
PioPetro
