Overview
In this 6-hour course, learners will dive into the essential principles of probability and statistics, which are foundational to data science and machine learning. Emphasizing a code-first approach, this course focuses on practical applications rather than isolated theory to equip learners with the skills necessary for real-world computational challenges.
What I will be able to do after this course
- Grasp key probability and statistics concepts essential for computer science and machine learning.
- Gain insights into distributions, entropy, and their relevance in data analysis.
- Understand and apply Bayesian inference in machine learning scenarios.
- Develop practical coding-based skills to exercise theoretical knowledge effectively.
- Strengthen intuition for data science through real-world examples and visualizations.
Course Instructor(s)
Dr. Mohammad Nauman is a seasoned educator and technologist specializing in computer science, data science, and machine learning. With extensive experience in both academia and industry applications, his teaching blends deep theoretical underpinnings with hands-on coding practices to ensure learners are well-prepared for future challenges.
Who is it for?
This course is ideal for beginner to intermediate developers eager to deepen their understanding of data analysis concepts, as well as individuals shifting into the data science or machine learning field who seek a practical, intuitive guide to probability and statistics fundamentals.
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