Chevron Left
Вернуться к Logistic Regression with Python and Numpy

Отзывы учащихся о курсе Logistic Regression with Python and Numpy от партнера Coursera Project Network

4.5
звезд
Оценки: 139
Рецензии: 24

О курсе

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....

Лучшие рецензии

DP
8 апр. 2020 г.

Want to do a project in Logistic Regression. You are at the right spot Don't delay and take the course.

MT
9 мар. 2020 г.

Easy to follow along, each step was made very clear, and I understood the justification behind steps.

Фильтр по:

1–24 из 24 отзывов о курсе Logistic Regression with Python and Numpy

автор: shiva s t

9 мар. 2020 г.

it is a great course and successfully trained my ml model

автор: Duddela S P

9 апр. 2020 г.

Want to do a project in Logistic Regression. You are at the right spot Don't delay and take the course.

автор: Megan T

10 мар. 2020 г.

Easy to follow along, each step was made very clear, and I understood the justification behind steps.

автор: Raj K

29 апр. 2020 г.

Great learning material and hands-on platform!

автор: Pranjal M

14 июня 2020 г.

A very good project for learners

автор: Thomas H

12 нояб. 2021 г.

great hand-on training

автор: Ashwin K

2 сент. 2020 г.

An amazing Project

автор: Gangone R

2 июля 2020 г.

very useful course

автор: JONNALA S R

7 мая 2020 г.

Good Initiation

автор: Nandivada P E

15 июня 2020 г.

super course

автор: Doss D

23 июня 2020 г.

Thank you

автор: Saikat K 1

7 сент. 2020 г.

Amazing

автор: Lahcene O M

3 мар. 2020 г.

Great

автор: tale p

27 июня 2020 г.

good

автор: p s

24 июня 2020 г.

Nice

автор: ANURAG P

5 июня 2020 г.

generally while using scikit-learn library for logistic regression, we don't really understand the classes and alogoriths behind what we import. This gives a clear view of what goes behind the imported scikit modules. Its pretty hard though as compared to sckit learn code but gives some deep knowledge about the numpy library

автор: Munna K

27 сент. 2020 г.

Well..I would like to recommend this project for machine learning students who can have a better understanding of concepts related to deep learning and Ml.

автор: Chow K M

4 окт. 2021 г.

I​t's implementation of gradient descent without the theory. Without the theory, it would not be understandable.

автор: Manzil-e A K

20 июля 2020 г.

I enjoyed it. Thank you. But helper functions could be explained more or given as a blog.

автор: Rosario P

23 сент. 2020 г.

Good course, very simple to understand

автор: Abdul Q

30 апр. 2020 г.

For beginners this course is great.

автор: Weerachai Y

8 июля 2020 г.

thanks

автор: Александр П

9 мар. 2020 г.

бестолковый курс, виртуальный стол неудобный, ноутбук неполный, нет модуля helpers

автор: Haofei M

4 мар. 2020 г.

totally waste of time. please go to enrol Anderw Ng courses about deep learning.