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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.

状态:Bayesian Statistics
状态:Descriptive Statistics
中级课程小时

精选评论

ZC

5.0评论日期: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!

RR

5.0评论日期:Nov 12, 2023

Very good course! Highly recommended to those who are just starting to learn mathematics for machine learning

YG

5.0评论日期:May 21, 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

EE

4.0评论日期: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!

HH

5.0评论日期:May 2, 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

AA

5.0评论日期: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

MR

5.0评论日期:Jun 1, 2024

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

LM

5.0评论日期:Feb 7, 2024

Excellent course. I studied probability and Stats long ago in university, but this course covered it in far greater depth.

SM

4.0评论日期:Aug 31, 2025

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

EW

5.0评论日期:Dec 16, 2024

Very thorough and give me necessary skill of probability and statistics that relevant in my job of data science and machine learning.

MB

5.0评论日期: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

5.0评论日期: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).

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