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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-machine-learning-a-z-hands-on-python/
课程评论：没有评论
**课程名称：** Machine Learning & Data Science A-Z: Hands-on Python 2024 **课程概述：** 本课程专为对数据科学和机器学习感兴趣但缺乏背景知识，或者害怕编程的学习者设计。即使您已了解机器学习，本课程也能帮助您复习所有技术并深入理解每个概念。 课程结构清晰，从基础概念入手，并逐步在 Python 中进行实现。主要内容包括： * **第一章：** 介绍与所需软件安装 * **第二章：** 常用的机器学习库（NumPy, Pandas & Matplotlib） * **第三章：** 数据预处理 * **第四章：** 机器学习的类型 * **第五章：** 监督学习：分类 * **第六章：** 监督学习：回归 * **第七章：** 无监督学习：聚类 * **第八章：** 模型调优 您将有机会使用不同的真实数据集来开发模型，并获得课程中编写的所有 Python 代码模板，方便下载和使用。 **课程亮点：** * **零基础友好：** 所有概念从基础讲解，Python 编程从零开始。 * **实战导向：** 结合真实数据集进行模型开发。 * **代码模板：** 提供课程中编写的所有 Python 代码，助力学习。 * **系统化学习：** 按照清晰的章节结构，循序渐进。 本课程旨在帮助您全面掌握机器学习和数据科学的基础知识和实践技能。
Are you interested in data science and machine learning, but you don't have any background, and you find the concepts confusing?Are you interested in programming in Python, but you always afraid of coding?I think this course is for you!Even if you are familiar with machine learning, this course can help you to review all the techniques and understand the concept behind each term.This course is completely categorized, and we don't start from the middle! We actually start from the concept of every term, and then we try to implement it in Python step by step. The structure of the course is as follows:Chapter1: Introduction and all required installationsChapter2: Useful Machine Learning libraries (NumPy, Pandas & Matplotlib)Chapter3: PreprocessingChapter4: Machine Learning TypesChapter5: Supervised Learning: ClassificationChapter6: Supervised Learning: RegressionChapter7: Unsupervised Learning: ClusteringChapter8: Model TuningFurthermore, you learn how to work with different real datasets and use them for developing your models. All the Python code templates that we write during the course together are available, and you can download them with the resource button of each section.Remember! That this course is created for you with any background as all the concepts will be explained from the basics! Also, the programming in Python will be explained from the basic coding, and you just need to know the syntax of Python.
