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Volver a Logistic Regression with NumPy and Python

Opiniones y comentarios de aprendices correspondientes a Logistic Regression with NumPy and Python por parte de Coursera Project Network

4.5
estrellas
379 calificaciones
47 reseña

Acerca del Curso

Welcome to this project-based course on Logistic with NumPy and Python. In this project, you will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn and statsmodels. The aim of this project and is to implement all the machinery, including gradient descent, cost function, and logistic regression, of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals. By the time you complete this project, you will be able to build a logistic regression model using Python and NumPy, conduct basic exploratory data analysis, and implement gradient descent from scratch. The prerequisites for this project are prior programming experience in Python and a basic understanding of machine learning theory. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, NumPy, and Seaborn pre-installed....

Principales reseñas

AS
29 de ago. de 2020

Very helpful for learning logistic regression without using any libraries. Before taking this project one should have a clear understanding of Logistic Regression, then it will be very helpful

CB
23 de may. de 2020

Its a good course. Instructor is good. Lot of concepts cleared and enough practice has done.

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1 - 25 de 47 revisiones para Logistic Regression with NumPy and Python

por Sambhaw S

2 de ago. de 2020

Excellent course but requires prior theoretical knowledge of logistic regression and linear regression. I have a suggestion for the instructor. If possible, can you attach conceptual videos that are already available on Coursera like liner regression lecture by Andrew Ng or any other lecture, then it will be beneficial for students. Overall a good project for starters like me.

Thank you

por Arnab S

30 de ago. de 2020

Very helpful for learning logistic regression without using any libraries. Before taking this project one should have a clear understanding of Logistic Regression, then it will be very helpful

por CHINMAY B

24 de may. de 2020

Its a good course. Instructor is good. Lot of concepts cleared and enough practice has done.

por Juan M B

7 de jun. de 2020

Great tool to practice what i learned in Andrew Yng's ML course about Log. Reg.

por Ramya G R

9 de jun. de 2020

I really enjoyed this course. Thank you for your valuable teaching.

por Punam P

4 de abr. de 2020

Thank You... Very nice and valuable knowledge provided.

por Thulasi R I 2 B 0

26 de sep. de 2020

Able to follow project. Thanks for guiding

por Mari M

14 de may. de 2020

Clear explanation and good content. Thanks

por Pulkit S

18 de jun. de 2020

good project got to learn a lot of things

por Shruti S

21 de jul. de 2020

Great course ! very informative

Thanks :)

por Krishna M T

12 de ago. de 2020

It is one of the best guided project.

por Melissa d C S

21 de jun. de 2020

Please, keep doing good job

por Pulkit D

16 de oct. de 2020

good course a lot to learn

por Erick M A

20 de jul. de 2020

Excelente aprovechamiento

por Pritam B

14 de may. de 2020

it was an nice experience

por Shreyas R

25 de abr. de 2020

Amazing. Must do this

por Diego R G

21 de may. de 2020

Great project!

por jagadeeswari N

28 de may. de 2020

nice overview

por Anisetti S K

23 de abr. de 2020

well balanced

por Ayesha N

16 de jun. de 2020

its was good

por Duy L Đ

13 de jul. de 2020

Really good

por Nandivada P E

15 de jun. de 2020

Nice course

por Dipak S s

24 de abr. de 2020

fine courxe

por Saikat K 1

8 de sep. de 2020

Amazing

por Dibyanshu S D

13 de ago. de 2020

Great