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Logistic Regression with Python and Numpy(으)로 돌아가기

Coursera Project Network의 Logistic Regression with Python and Numpy 학습자 리뷰 및 피드백

4.5
별점
139개의 평가
24개의 리뷰

강좌 소개

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....

최상위 리뷰

DP
2020년 4월 8일

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

MT
2020년 3월 9일

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

필터링 기준:

Logistic Regression with Python and Numpy의 24개 리뷰 중 1~24

교육 기관: shiva s t

2020년 3월 9일

it is a great course and successfully trained my ml model

교육 기관: Duddela S P

2020년 4월 9일

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

교육 기관: Megan T

2020년 3월 10일

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

교육 기관: Raj K

2020년 4월 29일

Great learning material and hands-on platform!

교육 기관: Pranjal M

2020년 6월 14일

A very good project for learners

교육 기관: Thomas H

2021년 11월 12일

great hand-on training

교육 기관: Ashwin K

2020년 9월 2일

An amazing Project

교육 기관: Gangone R

2020년 7월 2일

very useful course

교육 기관: JONNALA S R

2020년 5월 7일

Good Initiation

교육 기관: Nandivada P E

2020년 6월 15일

super course

교육 기관: Doss D

2020년 6월 23일

Thank you

교육 기관: Saikat K 1

2020년 9월 7일

Amazing

교육 기관: Lahcene O M

2020년 3월 3일

Great

교육 기관: tale p

2020년 6월 27일

good

교육 기관: p s

2020년 6월 24일

Nice

교육 기관: ANURAG P

2020년 6월 5일

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

교육 기관: Munna K

2020년 9월 27일

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.

교육 기관: Chow K M

2021년 10월 4일

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

교육 기관: Manzil-e A K

2020년 7월 20일

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

교육 기관: Rosario P

2020년 9월 23일

Good course, very simple to understand

교육 기관: Abdul Q

2020년 4월 30일

For beginners this course is great.

교육 기관: Weerachai Y

2020년 7월 8일

thanks

교육 기관: Александр П

2020년 3월 9일

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

교육 기관: Haofei M

2020년 3월 4일

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