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学生对 DeepLearning.AI 提供的 Calculus for Machine Learning and Data Science 的评价和反馈

4.8
946 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, learners will be able to: • Analytically optimize different types of functions commonly used in machine learning using properties of derivatives and gradients • Approximately optimize different types of functions commonly used in machine learning using first-order (gradient descent) and second-order (Newton’s method) iterative methods • Visually interpret differentiation of different types of functions commonly used in machine learning • Perform gradient descent in neural networks with different activation and cost functions 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

RM

Jan 3, 2025

It was a great learning experience, and all the examples were carefully chosen with a special focus on machine learning. Well done and thank you!

BF

Jun 1, 2023

Calculus is a very difficult topic and yet the manner in which this course is delivered makes everything so very easy to understand. Incredible.

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151 - Calculus for Machine Learning and Data Science 的 175 个评论(共 195 个)

创建者 Moaaz m

Aug 5, 2025

good

创建者 Anggi P S

Mar 23, 2024

good

创建者 Nidula R

Jul 6, 2023

good

创建者 Collins N

Aug 7, 2024

..

创建者 Nurullah K

Jun 25, 2024

It was good untill week 3. My real point is 3.5 . I think this spec is definitely not a math course. they just show the math parts of the ML , they are just telling ML terms, this is a calculus course but subjects are what a neural network is, what a gradient descent is , or network method etc. Where exactly math here? There is no need if you dont tell it comrehensively , Every tutor is teaching much more math than this spec in any ML course. You are telling %10 percent math and then %90 percent ML terminology. Why do i have to learn what a neural network even with more than two layer in a math course. Then what are you gonna tell me in the ML course??? if you will, then make it like first two weeks. It was like equally math and ML but in week 3 it is %90 like an ML Course.

创建者 Kevin W

Jul 10, 2024

Overall, it is an excellent introduction to calculus and applications to ML. However, some of the foundational explanations of differentiation need clarification. If you want a rigorous introduction to calculus, I recommend Khan's Academy. For example: in Khan's, you will find an in-depth explanation of limits which is only touched upon briefly in this course.

创建者 Adison

Aug 13, 2023

Rather simple introduction to Calculus in machine learning & data science. The course covers core concepts and linked well to applications such as Optimisation and Gradient descent.

The instructor provided good graphical visualisations to help learners understand and develop intuition on the concepts covered.

创建者 Aaron H

Oct 5, 2023

I actually understand gradient descent which is awesome. I need a little bit more practice running some of the problems to be proficient and remember how to do them, but I was able to complete them for the course and I suppose in real life I can just have my code (stolen from this course) find the answers.

创建者 Mahbod I

Jun 19, 2023

An excellent course indeed, although with some caveats.

I love the simplicity and the examples of the instructor. However, sometimes the materials needed to be more complex and exciting to watch. I definitely recommend this course to absolute beginners in calculus or someone who needs a refresher.

创建者 Yehan D

Aug 31, 2024

Content was extremely helpful but assignments were too simple. If you could make the graded content a little more challenging it might really help the student push himself further.

创建者 Kavit S

May 23, 2023

We had a great time learning this course. We really had some good sessions with friends while learnin this. We found about new concepts. Thanks

创建者 Amin N

Jun 15, 2023

Easy to follow for beginners. Concepts are well explained. I wish the Newtonian method had been explained in more details though.

创建者 Susy

Sep 2, 2023

The last programming assignment has some problems. If you touch the optional part, you get 0.

创建者 Arshad H

Mar 27, 2025

Very good course in terms of content. Labs and assignment env was a bit flaky when running.

创建者 Putri R N

Mar 21, 2024

It was so hard and challenging. I've nearly cried and somehow i passed. Thanks :")

创建者 Deleted A

Mar 5, 2023

Too easy for exercise, but the video lession is good, focused on ML perspective

创建者 G.nikhil k

May 25, 2024

It great course but the numpy will be new for some one who don't know to code.

创建者 Jathavan S

Jan 26, 2024

Labs could have been more difficult. Otherwise a good course for beginners.

创建者 Shaun S

Nov 13, 2023

Videos and explanations are great, but labs are rough.

创建者 geet c

Mar 23, 2023

Helpfully and covered all the topics related to ML

创建者 Evert J K

Nov 12, 2024

Good course and gives a good understanding!

创建者 Phu N

Apr 18, 2023

Notebook test case sometimes crashes

创建者 chaimaa E k

Mar 27, 2023

You are perfect Platform Coursera

创建者 马镓浚

Jul 15, 2023

Nice for review.