Overview
The course is an introduction to classical Machine Learning technique using Python and Scikit-Learn and neural network using PyTorch. It introduces learners to basic machine learning steps from data preparation to evaluation of machine learning models. Learners will learn and build two classical machine learning models namely Linear Regression and Logistic Regression for continuous and categorical data respectively. You will then be introduced to a simple neural network that is the foundation for deep learning. Learners will learn how to process data using Pandas library in Python as well as to visualise those data using Seaborn and Matplotlib. At the end, you will learn some metrics to evaluate your machine learning models.
Course details
For information on upcoming course dates or to register your interest, please click on [Apply / Register interest].
Duration: 2 days, 9.00 AM to 5.00 PM
Who should attend
Working professionals who are familiar with Python programming, computing or software engineering. This course is suitable for professionals with a small technical background who plan to enter the data science or artificial intelligence field. It is designed as a basic introduction before taking up the course “Fundamentals of Deep Learning and Neural Networks in PyTorch.”
Prerequisites
Participants should possess a basic understanding of the Python programming language and should have gone through the Fundamentals in Python (Basic) course.