Overview
The course is an introduction to classical Machine Learning technique using Python. 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 Pandas library in Python as well as to visualize those data using Seaborn and Matplotlib. On top of that, they will write the functions to build machine learning models using NumPy. Instead of using Scikit-Learn Library, learners will write their own machine learning functions to gain deeper understanding how such library functions. 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 and Part 1 of this course.