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DeepLearning.AI

Probability & Statistics for Machine Learning & Data Science

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.

Status: Descriptive Statistics
Status: Statistical Inference
IntermediateCourse33 hours

Featured reviews

ZC

5.0Reviewed Apr 15, 2026

The problem is I didn't code it on my own device, I just filled the programming exercises and it feels like I learned nothing but I learned a lot. The instructor is tireless!

WN

5.0Reviewed Oct 13, 2023

Great explanation. Easy to understand. The labs are understandable and very practical.

AY

4.0Reviewed Jul 1, 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.

JK

5.0Reviewed Oct 4, 2023

Best Course for statistics beginners. It saves tons of hours from digging book or sources.

MB

5.0Reviewed Mar 8, 2025

The course is very organized. It helped me go through the basics I needed with ML & DS in focus on how and why every part is used in ML applications.

EM

5.0Reviewed Aug 29, 2024

Clear, serious and captivating. I think that i now have a toolbox even though i was allergic to the matter (stats and prob).

AA

5.0Reviewed Sep 15, 2024

this course is amazing! this course teachs how important probabilities is in machine learning and covers alots of topics where probabilities and statistics are useful in machine learning

CW

5.0Reviewed Feb 4, 2025

Very fun intuitive way to learn probability and statistics. Mr. Serrano gave me an understanding of what the concepts truly mean and why which I have never had before. Thank you!

TJ

5.0Reviewed Sep 22, 2023

The course was very detailed and interactive, which made learning about statistics and probability easy. The engaging visuals were a great aid in understanding the concepts.

S

5.0Reviewed Jan 15, 2024

Perfect blend of Math and Python to have a Deep Basic foundation in Machine Learning and Data Science

J

5.0Reviewed Jan 21, 2025

Statistics involves a high cognitive load due to its complexity and abstract reasoning.

MR

5.0Reviewed Jun 1, 2024

Very nicely explained. Lab assignments provide a great opportunity to implement the concepts learnt on real world use cases.

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