学习路径描述

监督机器学习在 Python 中

掌握监督机器学习的基础，并了解如何使用带标签的数据进行预测。立即加入机器学习革命！如果你刚接触机器学习，或者想专攻监督式机器学习，这里是理想的起点。你将首先学习并实现核心监督学习模型，例如 K-Nearest Neighbors (KNN)、Logistic Regression、Linear Regression、Support Vector Machines (SVMs) 以及使用流行的 scikit-learn 库的基于树的模型。你还将了解如何使用像 XGBoost 这样的先进算法，高效提升表格数据集上的建模性能。为了充分发挥模型的最大效用，你将学习不同的超参数调优技术，以及如何根据你的使用场景决定采用哪种技术。你将通过将这些多样化模型的知识融会贯通，学习集成学习，即将不同模型组合起来以提升性能并解决更复杂的问题，从而完成该学习路径。完成后，你将掌握监督机器学习的核心概念，并能够在 Python 中加以应用。

先决条件

此学习路径无先决条件

Course
1
Supervised Learning with scikit-learn
Grow your machine learning skills with scikit-learn in Python. Use real-world datasets in this interactive course and learn how to make powerful predictions!
Project
额外
Predictive Modeling for Agriculture
Dive into agriculture using supervised machine learning and feature selection to aid farmers in crop cultivation and solve real-world problems.
Course
3
Linear Classifiers in Python
In this course you will learn the details of linear classifiers like logistic regression and SVM.
Course
4
Machine Learning with Tree-Based Models in Python
In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.
Project
额外
Predicting Movie Rental Durations
Build a regression model for a DVD rental firm to predict rental duration. Evaluate models to recommend the best one.
Course
6
Extreme Gradient Boosting with XGBoost
Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
Course
7
Hyperparameter Tuning in Python
Learn techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.
Course
8
Ensemble Methods in Python
Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.