Basic Statistics and Regression for Machine Learning in Python

In this 5-hour course, you will dive into the foundational concepts of machine learning statistics and regression using Python. From learning the basics of Python and essential libraries like NumPy, matplotlib, and scikit-learn, to mastering regression and data standardization techniques, this comprehensive guide is designed to help you build a strong understanding of statistical methods applied in machine learning.

What I will be able to do after this course

• Understand the basics of Python, including variables, functions, and libraries
• Analyze statistical measures such as central tendency and standard deviation
• Develop statistical models using regression techniques
• Implement Python-based solutions for analyzing data distributions
• Enhance machine learning model performance through data normalization

Course Instructor(s)

Abhilash Nelson is a seasoned data scientist and educator with extensive experience in mathematical modeling and programming for machine learning applications. He has a knack for distilling complex concepts into approachable content for learners at all levels. Through his practical-focused teaching methodology, learners gain both theoretical insights and hands-on experience for tackling statistical challenges in Python.

Who is it for?

This course is ideal for aspiring machine learning practitioners who are taking their first steps into the field. It suits individuals with no prior programming experience but an interest in understanding the mathematical foundation of machine learning. Learners aiming to grasp the statistical logic behind Python algorithms will particularly benefit. A willingness to learn and basic computer skills are the only prerequisites.