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Logistic Regression with Python and Numpy に戻る

Coursera Project Network による Logistic Regression with Python and Numpy の受講者のレビューおよびフィードバック

4.5
139件の評価
24件のレビュー

コースについて

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....

人気のレビュー

DP
2020年4月8日

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

MT
2020年3月9日

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

フィルター:

Logistic Regression with Python and Numpy: 1 - 24 / 24 レビュー

by shiva s t

2020年3月9日

it is a great course and successfully trained my ml model

by Duddela S P

2020年4月9日

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

by Megan T

2020年3月10日

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

by Raj K

2020年4月29日

Great learning material and hands-on platform!

by Pranjal M

2020年6月14日

A very good project for learners

by Thomas H

2021年11月12日

great hand-on training

by Ashwin K

2020年9月2日

An amazing Project

by Gangone R

2020年7月2日

very useful course

by JONNALA S R

2020年5月7日

Good Initiation

by Nandivada P E

2020年6月15日

super course

by Doss D

2020年6月23日

Thank you

by Saikat K 1

2020年9月7日

Amazing

by Lahcene O M

2020年3月3日

Great

by tale p

2020年6月27日

good

by p s

2020年6月24日

Nice

by ANURAG P

2020年6月5日

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

by Munna K

2020年9月27日

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.

by Chow K M

2021年10月4日

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

by Manzil-e A K

2020年7月20日

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

by Rosario P

2020年9月23日

Good course, very simple to understand

by Abdul Q

2020年4月30日

For beginners this course is great.

by Weerachai Y

2020年7月8日

thanks

by Александр П

2020年3月9日

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

by Haofei M

2020年3月4日

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