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Back to Probability & Statistics for Machine Learning & Data Science

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

4.6
stars
622 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鈥檒l 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: 鈥� Describe and quantify the uncertainty inherent in predictions made by machine learning models, using the concepts of probability, random variables, and probability distributions. 鈥� Visually and intuitively understand the properties of commonly used probability distributions in machine learning and data science like Bernoulli, Binomial, and Gaussian distributions 鈥� Apply common statistical methods like maximum likelihood estimation (MLE) and maximum a priori estimation (MAP) to machine learning problems 鈥� Assess the performance of machine learning models using interval estimates and margin of errors 鈥� Apply concepts of statistical hypothesis testing to commonly used tests in data science like AB testing 鈥� 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.聽 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鈥檒l use....

Top reviews

YG

May 22, 2025

It was very helpful course. It starts from the bare minimum but gradually you get to the point where you find yourself in Statistopia ???. Big applaud and thanks to Luis and also DeepLearning.AI

LC

May 3, 2024

Thanks DeepLearning.AI for creating this specialization. I went from the guy that everytime i hear the word 'math i'd freakout to 'math is my lover'. Once again thank you for everything

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101 - 125 of 127 Reviews for Probability & Statistics for Machine Learning & Data Science

By Nanda P N N

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Mar 23, 2024

COOLLLL

By Dwiki H

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Sep 29, 2023

so cool

By KURRA V T

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Jan 21, 2025

SUPERB

By Ni K P S

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Sep 28, 2023

Great!

By Nguyen N H L

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Aug 21, 2025

great

By Daniel K

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Apr 14, 2024

great

By Thansheeh K T

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Mar 22, 2025

good

By Sandesh L

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Mar 7, 2025

Best

By KURELLA R

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Sep 17, 2024

GOOD

By syabiroe

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Dec 1, 2023

nice

By Alif W S

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Oct 3, 2023

Good

By Adek P D

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Sep 30, 2023

nice

By TAMPAN S

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Oct 10, 2023

-

By Diego E M S

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Feb 15, 2025

I really enjoy this course, lern a lot of things, the content is very helpful and the quality of the video is good and the novel here, are the of the professor's examples to illustrate the concepts, however I would like to do a critic review about this course, I have would like to go deep in some concepts or prerequisites like t-student distribution, central limit theorem, law of large numbers , beta distribution and dirichelt distribution, I consider very important concept for the course thanks you very much, Profe Luis Serrano

By ADITYA V S

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Aug 6, 2024

Indepth and apt knwoledge about probability and statistics and their use-case in real life situations such as using confidence intervals which help you to understand the variablility, precision and reliability of your estimates. Also it has hypothesis testing that allows to verify your hypothesis based on data. Overall a very good course.

By Ericy

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Jun 18, 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!

By Anshul Y

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Jul 2, 2024

I think graded quiz was good, but the programming assignments could be made more challenging to have a good understanding of python and math simultaneously.

By Hadar D S (

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Jul 6, 2024

This is definitely the hardest course in this specialization. It's definitely interesting and will help me a lot at school.

By Salvador M

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Sep 2, 2025

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

By Makoto T

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Mar 29, 2025

I will recommend this course for everyone who want to have comprehensive and holistic view on data sciense.

By Evert J K

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Nov 12, 2024

Good course and gives a good understanding!

By Nenda M K M

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Oct 9, 2024

good course

By Sufi A Q Z S Q A

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Jul 21, 2025

good

By Beyers S

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Jan 23, 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