Code Along กับเราได้ที่
Code Along ↗Getting Started with Machine Learning in Python
Learn the fundamentals of supervised learning by using scikit-learn.
George Boorman
สร้างบัญชีฟรีของคุณ
ฝึกฝนและตรวจสอบความก้าวหน้าของคุณ
ดำเนินการต่อด้วย Googleเมื่อดำเนินการต่อ ถือว่าคุณยอมรับ ข้อกำหนดการใช้งานและ นโยบายความเป็นส่วนตัว และยอมรับว่าข้อมูลของคุณจะถูกจัดเก็บในสหรัฐอเมริกาที่เกี่ยวข้อง

cheat sheet
Scikit-Learn Cheat Sheet: Python Machine Learning
A handy scikit-learn cheat sheet to machine learning with Python, including some code examples.
บทแนะนำ
Python Machine Learning: Scikit-Learn Tutorial
An easy-to-follow scikit-learn tutorial that will help you get started with Python machine learning.
บทแนะนำ
Introduction to Machine Learning in Python
In this tutorial, you will be introduced to the world of Machine Learning (ML) with Python. To understand ML practically, you will be using a well-known machine learning algorithm called K-Nearest Neighbor (KNN) with Python.

Learn the fundamentals of supervised learning by using scikit-learn to make predictions about bank loans! You’ll discover how to build classification models that can predict loan application outcomes, as well as regression models to predict loan applicant salaries.
Learning by doing is encouraged, and you can follow along using DataLab. All you need is a DataCamp account. Note that members of some enterprise groups do not yet have access to use DataLab. Create a free DataCamp account with your personal email address to follow along.
We recommend that you have taken the following course before attending:
Introduction to Statistics with Python
Introduction to Data Visualization with Seaborn
Key takeaways
The importance of exploratory data analysis before performing supervised machine learning.
How to preprocess data for input to a machine learning model.
How to build and evaluate a classification model to predict loan application outcomes.