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所在平台: Udemy |
课程主页: https://www.udemy.com/course/python-machine-learning-jupyterlab/
课程评论：没有评论
课程名称：使用 Jupyter Lab 的 Python 机器学习入门 课程概述：如果您希望快速了解 Python 机器学习，那么这个课程正适合您。它旨在为初学者提供一个实践性的快速入门，通过使用 Python 和 JupyterLab 进行动手实验。我知道有些初学者只想了解机器学习的基本概念，而不想花太多时间在干燥的理论和数据清理上。因此，在本课程中，我们将跳过数据清理，所有数据集都已经被简化并清理好，您可以直接进入机器学习的实战。 机器学习（ML）是一种人工智能（AI）的形式，允许软件应用程序在没有明确编程的情况下变得更准确地预测结果。机器学习算法使用历史数据作为输入来预测新的输出值。Scikit-learn（也称为 sklearn）是一个开源的 Python 机器学习库，包含各种分类、回归和聚类算法。 Python 是一种高级、解释型的通用编程语言，其设计哲学强调代码的可读性，使用缩进来表示代码块。Python 是机器学习和人工智能的首选语言。JupyterLab 是最新的基于 web 的交互式开发环境，适用于笔记本、代码和数据。其灵活的界面允许用户在数据科学、科学计算、计算新闻和机器学习中配置和安排工作流程。 在这门入门课程中，我们将使用 Python 和 scikit-learn 进行简化的机器学习预测，我们将全部在被称为 Jupyter Lab 的基于 web 的工作空间中进行。与 Anaconda 相比，我选择 Jupyter Lab 是因为它的简单性，对初学者更加友好。使用 Jupyter Lab，任何 Python 模块的安装都可以通过 Python 的原生包管理器 pip 轻松完成，这极大简化了用户体验。 课程特点： - 简单和极简，直截了当 - 旨在为绝对初学者设计 - 快速和便捷的机器学习入门，使用线性回归 - 数据清理被省略，所有数据集均已清理 - 针对希望快速了解机器学习的人士 - 使用的所有工具（Jupyter Lab）完全免费 - 引入 Kaggle 以便进行进一步学习 学习目标：完成本课程后，您将： - 对机器学习有一个良好的初步了解 - 掌握 Jupyter Lab 和 Jupyter Notebook 的基本技能 - 为进一步学习更高级的机器学习主题做好准备 立即报名加入我们吧！
If you are looking for a fast and quick introduction to python machine learning, then this course is for you. It is designed to give beginners a quick practical introduction to machine learning by doing hands-on labs using python and JupyterLab. I know some beginners just want to know what machine learning is without too much dry theory and wasting time on data cleaning. So, in this course, we will skip data cleaning. All datasets is highly simplified already cleaned, so that you can just jump to machine learning directly.Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values. Scikit-learn (also known as sklearn) is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms.Python is a high-level, interpreted, general-purpose programming language. Its design philosophy emphasizes code readability with the use of indentations to signify code-blocks. It is also the language of choice for machine learning and artificial intelligence.JupyterLab is the latest web-based interactive development environment for notebooks, code, and data. Its flexible interface allows users to configure and arrange workflows in data science, scientific computing, computational journalism, and machine learning. Inside JupyterLab, we can create multiple notebooks. Each notebook for every machine learning project.In this introductory course, we will cover very simplified machine learning by using python and scikit-learn to do predictions. And we will perform machine learning all using the web-based interface workspace also known as Jupyter Lab. I have chosen Jupyter Lab for its simplicity compared to Anaconda which can be complicated for beginners. Using Jupyter Lab, installation of any python modules can be easily done using python's native package manager called pip. It simplifies the user experience a lot as compared to Anaconda. Features of this course:simplicity and minimalistic, direct to the pointdesigned with absolute beginners in mindquick and fast intro to machine learning using Linear Regressiondata cleaning is omitted as all datasets has been cleanedfor those who want a fast and quick way to get a taste of machine learningall tools (Jupyter Lab) used are completely freeintroduction to kaggle for further studiesLearning objectives:At the end of this course, you will:Have a very good taste of what machine learning is all aboutBe equipped with the fundamental skillsets of Jupyter Lab and Jupyter Notebook, andReady to undertake more advanced topics in Machine LearningEnroll now and I will see you inside!
