Python AI实战：20个GUI应用课程助你进阶专家_CG群

Khan School推出专家级Python AI与机器学习实战课，113节课时20小时，覆盖监督、无监督及关联规则，亲手部署20个AI应用，构建高价值作品集。

原始标题：Make 20 AI & Machine Learning GUI Applications in Python

Make 20 AI & Machine Learning GUI Applications in Python

由 Khan School 于 2026 年 9 月推出的《Python AI 与机器学习实战课程》，是一门专为有编程基础的开发者设计的专家级系统化视频教程。该课程总体量达 113 节课（总计 20 小时 16 分钟，约 8.3 GB），全程采用英语教学。课程不仅深入剖析特征、标签、模型评估等机器学习核心概念，还全面覆盖了监督学习（分类与回归）、无监督学习（聚类）以及关联规则挖掘（如购物篮分析）三大技术体系。其最大的亮点在于超强的实战性：学员将学习如何对真实世界的业务数据进行清洗与转换，并亲手用 Python 编写并部署 20 个功能完整的 AI 驱动应用程序。这不仅能帮助学员彻底打通从理论到工程落地的闭环，更能协助其构建起一个涵盖客户、员工及产品预测等多元化业务场景的高含金量作品集。

Build 20 AI-Powered Applications with Python Using Real-World Data and Machine Learning Algorithms

What you’ll learn
⚡ Understand the fundamentals of Artificial Intelligence and Machine Learning and how machine learning systems learn from data.
⚡ Learn the difference between supervised, unsupervised, and association-based learning and understand when to use each approach.
⚡ Master important Machine Learning concepts including features, labels, training data, testing data, models, predictions, and evaluation.
⚡ Learn how to prepare, clean, transform, and analyze datasets before feeding them into machine learning models.
⚡ Understand and implement important classification algorithms to predict categories and outcomes from data.
⚡ Learn regression techniques for predicting numerical values using real-world datasets.
⚡ Understand clustering algorithms and use them to discover hidden groups and patterns within datasets.
⚡ Learn association-rule mining with the Apriori algorithm and use Market Basket Analysis to discover products customers are likely to purchase together.
⚡ Build 20 complete AI and Machine Learning applications in Python, applying theoretical concepts to practical problems.
⚡ Learn how to train machine learning models, make predictions, and evaluate their performance using appropriate metrics.
⚡ Work with real-world datasets and understand how machine learning can be applied to problems involving customers, employees, products, predictions, and data ana
⚡ Build a portfolio of 20 practical AI and Machine Learning applications that demonstrate your ability to apply machine learning concepts using Python.

Requirements
❗ Basics of Python or any other Language is Required

Description
Are you ready to learnArtificial Intelligence and Machine Learning with Python and put your knowledge into practice by building real applications?

Make 20 AI & Machine Learning Applications in Python is a comprehensive, hands-on course designed to take you through both thetheory and practical implementation of Machine Learning. You won’t simply learn how to use machine learning libraries—you’ll also understand the concepts and algorithms behind the models and learn how to apply them to real-world problems.

Throughout the course, you’ll first develop a solid understanding of important Machine Learning concepts and techniques. You’ll learn how machine learning works, how models learn from data, how different types of machine learning problems are approached, and how to choose appropriate techniques for different situations.

You’ll explore important areas of Machine Learning includingsupervised learning, unsupervised learning, classification, regression, clustering, and association-rule learning. You’ll also learn about important concepts involved in preparing data, training models, making predictions, and evaluating model performance.

After learning the underlying concepts, you’ll put that knowledge into practice by building20 AI and Machine Learning applications in Python.

The projects will allow you to see how the concepts you learn can be applied to practical problems. You’ll work with real-world datasets and learn how to prepare and analyze data, train machine learning models, generate predictions, discover patterns, and interpret results.

You’ll work with different machine learning approaches and apply them to scenarios such as prediction, classification, customer analysis, employee performance analysis, and product purchasing patterns.

For example, you’ll build aMarket Basket Analysis application using the Apriori algorithm to discover which products customers are likely to purchase together. You’ll also build anemployee performance clustering application that uses clustering techniques to identify groups of employees based on their performance.

Throughout the course, you’ll work with Python and popular tools and libraries used in Machine Learning. You’ll learn how to take a machine learning problem fromraw data to a working application.

The combination of theory and practical projects is one of the key strengths of this course. Understanding the theory helps you knowwhy an algorithm works, while building applications teaches youhow to actually use it.

By the end of the course, you’ll have developed a strong foundation in Machine Learning concepts and gained hands-on experience by building20 complete AI and Machine Learning applications in Python.

You’ll also have a collection of practical projects that you can use to strengthen your portfolio and demonstrate your ability to apply Machine Learning to real-world problems.

Whether you’re a Python programmer, aspiring Machine Learning developer, student, data enthusiast, or someone looking to enter the world of AI, this course provides a combination oftheory, practical learning, and real-world application development.

Don’t just learn Machine Learning.

Understand it. Build with it. Apply it.
Join the course and start your journey into AI and Machine Learning with Python.

Who this course is for
⭐ Python programmers who want to learn how to apply Python to Artificial Intelligence and Machine Learning.
⭐ Beginners in Machine Learning who want to understand both the theory and practical implementation of ML algorithms.
⭐ Aspiring Machine Learning and AI developers who want hands-on experience working with real-world datasets.
⭐ Programmers who want to understand important ML techniques such as classification, regression, clustering, and association-rule learning.
⭐ Students interested in data science and predictive analytics who want to learn how machine learning can extract useful insights from data.
⭐ Learners who want practical experience with training models, making predictions, evaluating models, and interpreting results.
⭐ Students interested in customer and business analytics, including applications such as Market Basket Analysis and customer purchasing patterns.
⭐ Developers who want to learn how to turn machine learning models into practical Python applications.
⭐ Students who want to build a portfolio of 20 AI and Machine Learning projects that demonstrate practical ML skills.
⭐ Anyone who already has basic Python knowledge and wants to take the next step into Artificial Intelligence and Machine Learning through a combination of theory and hands-on development.

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