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返回到 Probability & Statistics for Machine Learning & Data Science

学生对 DeepLearning.AI 提供的 Probability & Statistics for Machine Learning & Data Science 的评价和反馈

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616 个评分

课程概述

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....

热门审阅

HZ

Jul 17, 2023

Nice recap of what we should now from university. The way of explanation with using examples was really helpful.

YG

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

筛选依据：

76 - Probability & Statistics for Machine Learning & Data Science 的 100 个评论（共 126 个）

创建者 DuNo

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Mar 21, 2024

harder to understand than first two courses

创建者 kaleem u

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Nov 12, 2023

Best course to learn Inferential Statistics

创建者 Tilakraj G

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Dec 22, 2024

Excellent content and excellent instructor

创建者 Ruoyan Q

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Sep 18, 2023

Great assignment, especially Week 1 and 4

创建者 Aditi S

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May 14, 2024

this course Is good for data structures

创建者 John P

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Oct 7, 2025

Great visualizations and explanations.

创建者 Haiyun H

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Aug 21, 2023

wonderful courses, Thank you very much

创建者 BHAGAVAN K

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Jan 20, 2025

Excellent course which I ever seen

创建者 Arvasu G

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Sep 20, 2025

A complete and sufficient course

创建者 Shaiq B

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Mar 14, 2025

Excellent foundational course.

创建者 Sasyesh P S C

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Dec 8, 2024

Excellent and exceptional

创建者 Andres F G A

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Jun 14, 2024

This course is a jewel!

创建者 Marc D

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Jul 11, 2024

Excellent didactic!

创建者 Ni P M O H P

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Sep 26, 2023

Thank You So Much!

创建者 Damian O S B

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Sep 17, 2025

AWESOME COURSE !!

创建者 Story B

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Sep 29, 2025

Well-structured

创建者 sara m

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Jun 9, 2024

amazing course

创建者 Anthony P

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Apr 13, 2025

Fantastic !!!

创建者 Muhammad F F

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Mar 27, 2024

Good material

创建者 Sabeur M

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Jan 15, 2024

Great cours

创建者 Aini N

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Sep 21, 2023

its amazing

创建者 Daniel G

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Apr 30, 2024

very nice

创建者 Robert P

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Apr 12, 2025

AMAZING!

创建者 Muhammad K I

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Mar 26, 2024

awesome