Overview
The course is an introduction to classical Machine Learning technique using Python and Scikit-Learn. 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. Learners will learn how to process data using R-squared Pandas library in Python as well as to visualize those data using Seaborn and Matplotlib. At the end, they will learn some metrics to evaluate their machine learning models.
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.