Introduction to Python Machine Learning using Jupyter Lab

如果您正在寻找快速快速的 Python 机器学习介绍，那么本课程适合您。它旨在通过使用 python 和 JupyterLab 进行动手实验，为初学者提供机器学习的快速实用介绍。我知道有些初学者只是想知道什么是机器学习，而不需要太多枯燥的理论和浪费时间在数据清理上。因此，在本课程中，我们将跳过数据清理。所有数据集都经过高度简化并已清理，因此您可以直接跳转到机器学习。

机器学习 (ML) 是一种人工智能 (AI)，它允许软件应用程序在预测结果方面变得更加准确，而无需明确编程。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 Python 编程语言的免费软件机器学习库。它具有各种分类、回归和聚类算法。

Python 是一种高级的、解释性的、通用的编程语言。它的设计理念强调代码的可读性，使用缩进来表示代码块。它也是机器学习和人工智能的首选语言。

JupyterLab 是最新的基于 Web 的笔记本、代码和数据交互式开发环境。其灵活的界面允许用户配置和安排数据科学、科学计算、计算新闻和机器学习方面的工作流。在 JupyterLab 中，我们可以创建多个笔记本。每个机器学习项目的每个笔记本。

在本入门课程中，我们将通过使用 python 和 scikit-learn 进行预测来介绍非常简单的机器学习。我们将使用基于 Web 的界面工作区（也称为 Jupyter Lab）执行机器学习。与 Anaconda 相比，我选择 Jupyter Lab 的简单性对于初学者来说可能很复杂。使用 Jupyter Lab，任何 python 模块的安装都可以使用 python 的本地包管理器 pip 轻松完成。与 Anaconda 相比，它大大简化了用户体验。

A quick introduction to machine learning using python scikit-learn linear regression for modelling and prediction

Published 05/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 15 lectures (2h 35m) | Size: 965 MB

What you’ll learn
Python 3
Exploratory data analysis and visualizations
Machine learning
Building prediction models
Linear regression
Evaluating models
Creating Jupyter notebooks in Jupyter Lab
Common python operations in Jupypter notebooks
Using scikit-learn for machine learning
and more…

Requirements
Windows PC
No programming experience needed. You will learn everything you need to know.

Description
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 point

designed with absolute beginners in mind

quick and fast intro to machine learning using Linear Regression

data cleaning is omitted as all datasets has been cleaned

for those who want a fast and quick way to get a taste of machine learning

all tools (Jupyter Lab) used are completely free

introduction to kaggle for further studies

Learning objectives

At the end of this course, you will

Have a very good taste of what machine learning is all about

Be equipped with the fundamental skillsets of Jupyter Lab and Jupyter Notebook, and

Ready to undertake more advanced topics in Machine Learning

Who this course is for
Anyone wanting to get a quick taste hands-on machine learning
Complete beginners to machine learning
Anyone wanting to learn how to create Jupyter Notebooks using Jupyter Lab instead of Anaconda