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Learner Reviews & Feedback for Probability & Statistics for Machine Learning & Data Science by DeepLearning.AI

4.6
stars
671 ratings

About the Course

Newly updated for 2024! Mathematics for Machine Learning and Data Science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. In machine learning, you apply math concepts through programming. And so, in this specialization, you’ll apply the math concepts you learn using Python programming in hands-on lab exercises. As a learner in this program, you'll need basic to intermediate Python programming skills to be successful. After completing this course, you will be able to: • Describe and quantify the uncertainty inherent in predictions made by machine learning models, using the concepts of probability, random variables, and probability distributions. • Visually and intuitively understand the properties of commonly used probability distributions in machine learning and data science like Bernoulli, Binomial, and Gaussian distributions • Apply common statistical methods like maximum likelihood estimation (MLE) and maximum a priori estimation (MAP) to machine learning problems • Assess the performance of machine learning models using interval estimates and margin of errors • Apply concepts of statistical hypothesis testing to commonly used tests in data science like AB testing • Perform Exploratory Data Analysis on a dataset to find, validate, and quantify patterns. Many machine learning engineers and data scientists need help with mathematics, and even experienced practitioners can feel held back by a lack of math skills. This Specialization uses innovative pedagogy in mathematics to help you learn quickly and intuitively, with courses that use easy-to-follow visualizations to help you see how the math behind machine learning actually works.  We recommend you have a high school level of mathematics (functions, basic algebra) and familiarity with programming (data structures, loops, functions, conditional statements, debugging). Assignments and labs are written in Python but the course introduces all the machine learning libraries you’ll use....

Top reviews

SM

Aug 31, 2025

This course is basically good, but it should be added some difficult topic, such as SVD in Linear Algebra,...

EE

Jun 17, 2024

Very thorough and easy to comprehend approach to learning statistical and probability theory which is important foundational knowledge, not just in ML but any field of data analytics!

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126 - 131 of 131 Reviews for Probability & Statistics for Machine Learning & Data Science

By Sufi A Q Z S Q A

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Jul 21, 2025

good

By Luis H

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Dec 4, 2025

Overall good content but some content inconsistencies that confuse learners a lot: ambiguous questioning/answers, typos, wrong teaching at some points (X is correct and it's also shown in slides but the narrator says Y). Towards the end lots of concepts are left unexplained thoroughly, so you are forced to re-watch the lessons again 5 times or look the info somewhere else.

By Beyers S

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Jan 22, 2024

Cannot finish this course, as my Python skills are lacking - yet, the course was advertised as for "beginners". I tried to submit my first programming assignment about 30 times, no success. Very disappointed.

By B21DCCN632 _ N V Q

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Jul 11, 2024

it very hard to learn

By Sanidhya P

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Nov 1, 2025

The explanation was really bad. It was more of a bookish reading without associating with real world example or telling the why. I had to lookup and depend on other resources.

By Sameer S

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Jan 5, 2025

My week 1 assignment is running perfectly fine, but when submitted for grading, it is showing error that the CSV file is not found. I have the following questions: 1. I can see the CSV file, so why is it not loading. 2. If it is not present, how come sections 2 & 3 are successful 3. Why only sections 1 & 4 are issues? 4. I have submitted a ticket for it, but still no response. Kindly check and let me know.