10最好的受监督的机器学习教程推荐

"This post includes affiliate links for which I may make a small commission at no extra cost to you should you make a purchase."

特写 iPhone，显示 Udemy 应用程序和带笔记本的笔记本电脑有数以千计的在线课程和课程可以帮助您提高 受监督的机器学习 技能并获得 受监督的机器学习 证书。

在这篇博客文章中，我们的专家汇总了 10 个精选列表 最好的 受监督的机器学习 课程, 现在在线提供的教程、培训计划、课程和认证。

我们只包括那些符合我们高质量标准的课程。我们花了很多时间和精力来为您收集这些。这些课程适合所有级别的初学者、中级学习者和专家。

以下是这些课程以及它们为您提供的内容！

10最好的受监督的机器学习教程推荐

课程名 在校生（人数） 评论（计数）
1. Data Science: Supervised Machine Learning in Python 我们的最佳选择 19082+ 2486+
2. Supervised Machine Learning From First Principles 8993+ 23+
3. Supervised Learning for AI with Python and Tensorflow 2 3433+ 25+
4. Machine Supervised Learning: Regression in Python 3 and Math 3076+ 25+
5. The Complete Supervised Machine Learning Models in Python 2007+ 130+
6. Classification Models: Supervised Machine Learning in Python 1006+ 4+
7. The Supervised Machine Learning Bootcamp 583+ 16+
8. Supervised Machine Learning for beginners 152+ 41+
9. Supervised Machine Learning in Python 77+ 8+
10. Machine Learning (Simply Explained by a Data Scientist) 2+ 1+

1. Data Science: Supervised Machine Learning in Python 经过 “Lazy Programmer Team, Lazy Programmer Inc.” Udemy课程 我们的最佳选择

Full Guide to Implementing Classic Machine Learning Algorithms in Python and with Scikit-Learn

截至目前，超过 19082+ 人们已经注册了这门课程，而且已经结束了 2486+ 评论.

课程内容
Introduction and Review
K-Nearest Neighbor
Naive Bayes and Bayes Classifiers
Decision Trees
Perceptrons
Practical Machine Learning
Building a Machine Learning Web Service
Conclusion
Setting Up Your Environment (FAQ by Student Request)
Extra Help With Python Coding for Beginners (FAQ by Student Request)
Effective Learning Strategies for Machine Learning (FAQ by Student Request)
Appendix / FAQ Finale

单击此处获得 95% OFF 折扣，当您单击时将自动应用折扣

2. Supervised Machine Learning From First Principles 经过 Houston Muzamhindo Udemy课程

Discussing the principles behind the most used Machine Learning algorithms

截至目前，超过 8993+ 人们已经注册了这门课程，而且已经结束了 23+ 评论.

课程内容
Introduction to Machine Learning
Introduction to Statistical Learning
Linear Regression
Classification
Validation and The Bootstrap Methods
Linear Model Selection and Regularization
Tree Based Methods

单击此处获得 95% OFF 折扣，当您单击时将自动应用折扣

3. Supervised Learning for AI with Python and Tensorflow 2 经过 Jeremy Richard Lai Hong Udemy课程

Uncover the Concepts and Techniques to Build and Train your own Artificial Intelligence Models

截至目前，超过 3433+ 人们已经注册了这门课程，而且已经结束了 25+ 评论.

课程内容
Introduction
The Basics
Feedforward Neural Networks
Convolutional Neural Networks
Sequential Data
Conclusion

单击此处获得 95% OFF 折扣，当您单击时将自动应用折扣

4. Machine Supervised Learning: Regression in Python 3 and Math 经过 Ahmed Attia Udemy课程

Master Regression Algorithm as it provides a base for you to build on and learn other ML algorithms.

截至目前，超过 3076+ 人们已经注册了这门课程，而且已经结束了 25+ 评论.

课程内容
Simple Linear Regression
Multiple Linear Regression
Ridge & Lasso Regression
Polynomial Regression
Decision Trees & Random Forests Regression

单击此处获得 95% OFF 折扣，当您单击时将自动应用折扣

5. The Complete Supervised Machine Learning Models in Python 经过 Data Science Academy Udemy课程

Learn the Intuition and Math behind Every Model

截至目前，超过 2007+ 人们已经注册了这门课程，而且已经结束了 130+ 评论.

课程内容
Introduction
Simple Linear Regression Model
Simple Linear Regression implementation in Python
Multiple Linear Regression Model Intuitions
Multiple Linear Regression Model implementation in Python
Polynomial Regression Model Intuitions
Polynomial Regression Model implementation in Python
Ridge Regression Model Intuitions
Ridge Regression implementation in Python
Lasso Regression Model Intuition
Lasso Regression implementation in Python
Decision Tree Regression Model Intuition
Decision Tree Regression implementation in Python
Random Forest Regression Model Intuition
Random Forest Regression implementation in Python
K Nearest Neighbors Model
K Nearest Neighbors implementation in Python
Logistic Regression Model
Logistic Regression implementation in Python
Decision Tree Classification Model
Decision Tree Classification implementation in Python
Random Forest Classification Model
Random Forest Classification implementation in Python
The Naive Bayes Classification Model
Naive Bayes implementation in Python
Support Vector Classification Model
Support Vector implementation in Python

单击此处获得 95% OFF 折扣，当您单击时将自动应用折扣

6. Classification Models: Supervised Machine Learning in Python 经过 Karthik K Udemy课程

A Quick Way to Learn and Implement Classification AI Algorithms in Python. A Course for Beginners.

截至目前，超过 1006+ 人们已经注册了这门课程，而且已经结束了 4+ 评论.

课程内容
Fundamentals
Building and Evaluating Classification ML Models

单击此处获得 95% OFF 折扣，当您单击时将自动应用折扣

7. The Supervised Machine Learning Bootcamp 经过 365 Careers Udemy课程

“Data Science, Python, sk learn, Decision Trees, Random Forests, KNNs, Ridge Lasso Regression, SVMs”

截至目前，超过 583+ 人们已经注册了这门课程，而且已经结束了 16+ 评论.

课程内容
Introduction
Setting up the Environment
Naïve Bayes
K-Nearest Neighbors
Decision Trees and Random Forests
Support Vector Machines
Ridge and Lasso Regression

单击此处获得 95% OFF 折扣，当您单击时将自动应用折扣

8. Supervised Machine Learning for beginners 经过 Ro Science Udemy课程

“kick start your machine learning journey with supervised learning for beginners, python, jupyter and scikit-learn!”

截至目前，超过 152+ 人们已经注册了这门课程，而且已经结束了 41+ 评论.

课程内容
Introduction
Classification problems
Data analysis and preparation
Model testing and evaluation
Linear models
K nearest neighbors
Decision trees and random forests
Conclusion
Appendix

单击此处获得 95% OFF 折扣，当您单击时将自动应用折扣

9. Supervised Machine Learning in Python 经过 Gianluca Malato Udemy课程

A practical course about supervised machine learning using Python programming language

截至目前，超过 77+ 人们已经注册了这门课程，而且已经结束了 8+ 评论.

课程内容
Introduction to supervised machine learning
The tools used in this course
Linear models
Decision trees
K-nearest neighbors
Naive Bayes
Support Vector Machines
Neural Networks
Introduction to ensemble models
Ensemble models: bagging
Ensemble models: boosting
Ensemble models: voting
Ensemble models: stacking
Performance evaluation
Cross-Validation and hyperparameter tuning
Feature importance and model interpretation
Recursive Feature Elimination
Practical examples in Python
Persisting our model
Practical approaches

单击此处获得 95% OFF 折扣，当您单击时将自动应用折扣

10. Machine Learning (Simply Explained by a Data Scientist) 经过 CryptoShare Wealth Institute Udemy课程

Learn how Machine Learning actually works!

截至目前，超过 2+ 人们已经注册了这门课程，而且已经结束了 1+ 评论.

课程内容
Intro to Machine Learning
Supervised Machine Learning
Unsupervised Machine Learning
Reinforcement Machine Learning

单击此处获得 95% OFF 折扣，当您单击时将自动应用折扣

下面是一些关于学习的常见问题受监督的机器学习

学习受监督的机器学习需要多长时间？

“学习受监督的机器学习需要多长时间”这个问题的答案是。 . .这取决于。每个人都有不同的需求，每个人都在不同的场景下工作，所以一个人的答案可能与另一个人的答案完全不同。

考虑这些问题：你想学习 受监督的机器学习 是为了什么？你的出发点在哪里？您是初学者还是有使用 受监督的机器学习 的经验？你能练习多少？每天1小时？每周40小时？ 查看本课程关于 受监督的机器学习.

受监督的机器学习 学起来容易还是难？

不，学习 受监督的机器学习 对大多数人来说并不难。检查这个 关于如何学习的课程 受监督的机器学习 立刻！

如何快速学习受监督的机器学习？

学习 受监督的机器学习 最快的方法是先得到这个 受监督的机器学习 课程, 然后尽可能练习你学到的任何东西。即使每天只有 15 分钟的练习。一致性是关键.

在哪里学习 受监督的机器学习？

如果您想探索和学习 受监督的机器学习，那么 Udemy 为您提供了学习 受监督的机器学习 的最佳平台。查看此 关于如何学习的课程 受监督的机器学习 立刻！