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Learner Reviews & Feedback for Calculus for Machine Learning and Data Science by DeepLearning.AI

4.8
stars
682 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’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....

Top reviews

PB

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great course for an intro or refresher respecting optimization, regression, and classification

PK

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very very structured. Cant be more thankful to initiatives of Louis Serrano and Andrew NG, What a wonderful human service. Blessings from India

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126 - 146 of 146 Reviews for Calculus for Machine Learning and Data Science

By Putri R N M

Mar 21, 2024

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

By Deleted A

Mar 5, 2023

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

By G.nikhil k

May 25, 2024

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

By Jathavan S

Jan 26, 2024

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

By Shaun S

Nov 13, 2023

Videos and explanations are great, but labs are rough.

By geet c

Mar 23, 2023

Helpfully and covered all the topics related to ML

By Phu N

Apr 18, 2023

Notebook test case sometimes crashes

By chaimaa E k

Mar 27, 2023

You are perfect Platform Coursera

By 马镓浚

Jul 15, 2023

Nice for review.

By Orson T M

Apr 9, 2023

Good Professor !

By Shaheen B

Aug 6, 2024

The material was great and very informative, and intuitive. That said, there were a few times when 1) the lab didn't align with the learnings - multiple variables were used in labs before described in later lectures. 2) The final quiz asked questions about using Newton's method approximation recursive formula, but the language used was very different than how the concept was taught in previous lessons. If mentors/contributors can look through the lessons/labs/quiz and ensure there is fluidity, that would be very helpful. Thank you!

By Robert B

Jul 16, 2023

Much time spent on Python in the labs that did not result in learning Python.

The labs make it clear that Python is VERY important, but I suggest a separate Python course to be taken first (even before the linear algebra material). Even making it into a four-course specialization is a far better use of one's time than struggling with labs that only8 illustrate, not teach, this critical tool.

By Nicholas J F

Aug 15, 2023

Good content giving insight to the mathematical foundations of Machine Learning.

However the Python is horribly outdated, back at 3.8; which will sunset in a year and a few months.

Some of the examples are not PEP 8 compliant.

That aside it is good to really understand from where the machinations of neural networks derive.

By Arta A

Dec 18, 2023

Useful for beginners and fundamental concepts. Before starting the course I thought that the course will be helpful in professional journey but I found out that the basic concepts are discussed.

By Evgeny A

Aug 3, 2023

Videos: great, easy to follow

Labs: not so great

By Daniel K

Apr 14, 2024

too easy

By andrew g

Apr 12, 2024

Not possible without knowing Python 3 already. Went over easy topics too slowly and harder topics too quickly. Barely talking about ML so I'm still not sure how much of this relate.

By Ehsan s

May 26, 2024

better than linear algebra course.

By Omar A B

Apr 30, 2023

the single start is definitely more than this course deserves, the specialization should be Titled Math for elementary school or for Kids. the explanation is soo naive and topics are not well covered! please Do NOT waste your TIME going through This course as the overall gain is almost ~0.000001

By Michael S

Nov 17, 2023

this is defenitly not helping beginners , topics are not covered in an easy way , labs are a complete nightmare , defenitly made me hate machine learning , not help me understand it

By Chawanwat J

Sep 9, 2023

Cancel lab please, It's too complicated and has a lot of unclear questions