Probability & Statistics for Machine Learning & Data Science
Master fundamental probability and statistical concepts essential for machine learning with practical Python applications.
Master fundamental probability and statistical concepts essential for machine learning with practical Python applications.
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.
4.6
(447 ratings)
61,372 already enrolled
Instructors:
English
پښتو, বাংলা, اردو, 2 more
Powered by
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
Shareable certificate
Closed caption
Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
Created by
Provided by
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
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
Instructor
Quantum AI Research Scientist and Educator
Luis Serrano is an accomplished AI scientist, popular YouTuber, and author of the book "Grokking Machine Learning." Currently, he serves as a quantum AI research scientist at Zapata Computing in Toronto, where he develops machine learning algorithms for quantum computers. Previously, he held significant roles in Silicon Valley, including lead AI educator at Apple, head of content for AI and Data Science at Udacity, and a member of the video recommendations team at Google’s YouTube. With a strong academic background, including a Bachelor's and Master's from the University of Waterloo and a PhD from the University of Michigan, Luis has a deep passion for mathematics that began in his youth when he represented Colombia in the International Mathematical Olympiads. Through his work and online presence, he aims to make complex AI concepts accessible to a broader audience while continuing to contribute to the advancement of quantum computing.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
4.6 course rating
447 ratings
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.