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Probability & Statistics for Machine Learning & Data Science
Master fundamental probability and statistical concepts essential for machine learning with practical Python applications.
Course Cost
Free course
Intermediate
Skill Level
31 Hours
Self-paced lessons
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Mathematics for Machine Learning and Data Science Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
61,372 Enrolled
English
What you'll learn
Understand and apply probability distributions in ML
Master statistical estimation methods
Conduct hypothesis testing and AB testing
Perform exploratory data analysis
Quantify uncertainty in ML predictions
Skills you'll gain
This course includes:
8.4 Hours PreRecorded video
7 quizzes, 1 assignment
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course explores probability and statistics fundamentals crucial for machine learning and data science. Students learn to quantify uncertainty in ML predictions, understand probability distributions, apply estimation methods like MLE and MAP, and conduct statistical hypothesis testing. The curriculum combines theoretical concepts with practical Python implementation, preparing learners for real-world data analysis and machine learning applications.
Introduction to Probability and Probability Distributions
Module 1 · 12 Hours to complete
Describing probability distributions and probability distributions with multiple variables
Module 2 · 8 Hours to complete
Sampling and Point estimation
Module 3 · 5 Hours to complete
Confidence Intervals and Hypothesis testing
Module 4 · 6 Hours to complete
Fee Structure
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Faculties
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Frequently asked Questions
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