Python中的线性回归和逻辑回归 Linear Regression and Logistic Regression in Python

在没有编码或数学背景的情况下，建立预测性最大似然模型。初学者的线性回归和逻辑回归

你会学到:
了解如何使用线性和逻辑回归技术解决现实生活中的问题
在运行回归分析之前，使用单变量和双变量分析对数据进行初步分析
了解如何解释线性和逻辑回归模型的结果，并将其转化为可行的见解
深入了解线性和逻辑回归问题的数据收集和数据预处理
Python中使用Numpy库的基本统计
用Python中的Seaborn库表示数据
基于Python的Scikit Learn和stats模型库的机器学习线性回归技术

时长:8h 21m |视频:. MP4，1280×720 30 fps |音频:AAC，44.1 kHz，2ch |大小解压后:2.63 GB
语言：英语+中英文字幕（云桥网络 机译）

要求:
本课程从基础开始，您甚至不需要编码背景来用Python构建这些模型
学生将需要安装Python和Anaconda软件，但是我们有一个单独的讲座来帮助您安装相同的软件

描述:
您正在寻找一个完整的线性回归和逻辑回归课程，该课程会教您用Python创建线性或逻辑回归模型所需的一切，对吗？
你找到了正确的线性回归课程！
完成本课程后，您将能够:
确定可以使用机器学习的线性和逻辑回归技术解决的业务问题。
用Python创建一个线性回归和逻辑回归模型，并分析其结果。
自信地建模和解决回归和分类问题
一份可验证的结业证书将提交给所有学习机器学习基础课程的学生。

本课程涵盖哪些内容？
本课程教您创建线性回归模型的所有步骤，线性回归模型是最流行的机器学习模型，用于解决业务问题。

以下是线性回归课程的课程内容:

第1部分-统计基础
本节分为五个不同的讲座，从数据类型开始，然后是统计类型
然后是描述数据的图形表示，然后是关于中心平均值等度量的讲座
中值和模式，最后是离差的度量，如范围和标准偏差

第2节- Python基础
本节让您开始使用Python。
这一部分将帮助您在系统上设置python和Jupyter环境，它将教授
您将了解如何在Python中执行一些基本操作。我们将了解不同图书馆的重要性，如Numpy、Pandas & Seaborn。

第3部分-机器学习介绍
在本节中，我们将学习-机器学习是什么意思。与机器学习相关的含义或不同术语是什么？您将看到一些例子，以便了解机器学习实际上是什么。它还包含建立机器学习模型的步骤，不仅仅是线性模型，任何机器学习模型。

第4节-数据预处理
在本节中，您将了解需要采取哪些措施来逐步获取数据，然后
为分析做准备这些步骤非常重要。
我们从了解业务知识的重要性开始，然后我们将看到如何进行数据探索。我们学习如何进行单变量分析和双变量分析，然后我们讨论异常值处理、缺失值插补、变量转换和相关性等主题。

第5节-回归模型
本节从简单的线性回归开始，然后介绍多元线性回归。
我们已经涵盖了每个概念背后的基本理论，但没有太多的数学知识
理解这个概念来自哪里，以及它有多重要。但是即使你不明白
只要你学会如何运行和解释在实践课中教授的结果，一切都会好的。
我们还将研究如何量化模型的准确性，F统计的含义是什么，自变量数据集中的分类变量在结果中是如何解释的，普通最小二乘法还有哪些变化，以及我们最终如何解释结果以找出业务问题的答案。

到本课程结束时，您在Python中创建回归模型的信心将会飙升。您将彻底了解如何使用回归建模来创建预测模型和解决业务问题。

这门课对你有什么帮助？
如果你是一名业务经理或高管，或者是一名想在现实世界的业务问题中学习和应用机器学习的学生，本课程将通过教你最流行的机器学习技术，即线性回归和逻辑回归，为你提供坚实的基础
为什么要选择这门课？
本课程涵盖了通过线性和逻辑回归解决业务问题时应该采取的所有步骤。
大多数课程只侧重于教授如何运行分析，但我们认为运行分析前后发生的事情甚至更重要，即在运行分析之前，拥有正确的数据并对其进行一些预处理非常重要。在运行分析之后，您应该能够判断您的模型有多好，并将结果解释为实际上能够帮助您的业务。

我们凭什么有资格教你？
该课程由Abhishek和Pukhraj教授。作为全球分析咨询公司的经理，我们已经使用机器学习技术帮助企业解决了他们的业务问题，我们已经利用我们的经验将数据分析的实际方面纳入了本课程
我们也是一些最受欢迎的在线课程的创作者，有超过15万的注册人数和数以千计的5星级评价，例如:
这很好，我喜欢所有给出的解释都能被一个外行人理解的事实——约书亚
谢谢作者的精彩课程。你是最棒的，这门课值得任何代价。黛西
我们的承诺
教育我们的学生是我们的工作，我们致力于此。如果您对课程内容、练习单或任何相关主题有任何疑问，您可以随时在课程中发布问题或直接向我们发送消息。
下载练习文件，参加测验，完成作业
每堂课都附有课堂笔记，供你跟随。你也可以参加测验来检查你对概念的理解。每个部分都包含一个练习作业，让你切实地完成你的学习。

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以下是想要开始机器学习之旅的学生的常见问题列表-

什么是机器学习？
机器学习是计算机科学的一个领域，它使计算机能够在没有明确编程的情况下学习。它是人工智能的一个分支，基于系统可以从数据中学习、识别模式并在最少的人为干预下做出决策的思想。

机器学习的线性回归技术是什么？
线性回归是回归问题的简单机器学习模型，即当目标变量为实值时。

线性回归是一种线性模型，例如，假设输入变量(x)和单个输出变量(y)之间存在线性关系的模型。更具体地说，y可以从输入变量(x)的线性组合中计算出来。

当有单个输入变量(x)时，该方法称为简单线性回归。
当有多个输入变量时，该方法称为多元线性回归。

为什么要学习机器学习的线性回归技术？
学习机器学习的线性回归技术有四个原因:
1.线性回归是最流行的机器学习技术
2.线性回归具有较好的预测精度
3.线性回归易于实现和解释
4.它给你一个坚实的基础，开始学习机器学习的其他先进技术

学习机器学习的线性回归技术需要多长时间？
线性回归很容易，但没有人能确定它需要的学习时间。这完全取决于你。我们采用的帮助您学习线性回归的方法从基础开始，在几个小时内将您带到高级水平。你可以照着做，但是记住不练习你什么也学不到。练习是记住你所学的一切的唯一方法。因此，我们还为您提供了另一个数据集，作为单独的线性回归项目。

为了能够构建机器学习模型，我应该遵循哪些步骤？

你可以把你的学习过程分成4个部分:

统计学和概率-实现机器学习技术需要统计学和概率概念的基础知识。课程的第二部分涵盖了这一部分。

理解机器学习-第四部分帮助您理解与机器学习相关的术语和概念，并为您提供构建机器学习模型应遵循的步骤

编程经验——机器学习的一个重要部分是编程。Python和R显然是最近几天脱颖而出的领头羊。第三部分将帮助您设置Python环境，并教您一些基本操作。在后面的部分中，有一个视频介绍了如何实现Python理论课中教授的每个概念

对线性和逻辑回归建模的理解-对线性和逻辑回归有很好的了解可以让你对机器学习是如何工作的有一个坚实的理解。尽管线性回归是机器学习中最简单的技术，但它仍然是最受欢迎的技术，具有相当好的预测能力。第五和第六部分从头到尾涵盖线性回归主题，每个理论讲座都有一个相应的实践讲座，我们实际上与您一起运行每个查询。

为什么使用Python进行数据机器学习？
理解Python是机器学习职业所需的宝贵技能之一。
尽管并非一直如此，Python是数据科学的首选编程语言。这里有一段简短的历史:
2016年，它在数据科学竞赛的首要平台Kaggle上超越了R。
2017年，在kd掘金对数据科学家最常用工具的年度调查中，它超过了R。
2018年，66%的数据科学家报告每天使用Python，使其成为分析专业人士的头号工具。
机器学习专家预计，随着Python生态系统的不断发展，这一趋势将继续下去。虽然您学习Python编程的旅程可能才刚刚开始，但很高兴知道就业机会也很丰富(并且还在增长)。

这门课是给谁上的:
追求数据科学职业的人
专业工作人员开始他们的数据之旅
需要更多实践经验的统计学家
任何想在短时间内从初级到高级掌握线性和逻辑回归的人

Duration: 8h 21m | Video: .MP4, 1280×720 30 fps | Audio: AAC, 44.1 kHz, 2ch | Size: 2.48 GB
Genre: eLearning | Language: English

Build predictive ML models with no coding or maths background. Linear Regression and Logistic Regression for beginners

What you’ll learn:
Learn how to solve real life problem using the Linear and Logistic Regression technique
Preliminary analysis of data using Univariate and Bivariate analysis before running regression analysis
Understand how to interpret the result of Linear and Logistic Regression model and translate them into actionable insight
Indepth knowledge of data collection and data preprocessing for Linear and Logistic Regression problem
Basic statistics using Numpy library in Python
Data representation using Seaborn library in Python
Linear Regression technique of Machine Learning using Scikit Learn and Statsmodel libraries of Python

Requirements:
This course starts from basics and you do not even need coding background to build these models in Python
Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same

Description:
You’re looking for a complete Linear Regression and Logistic Regression course that teaches you everything you need to create a Linear or Logistic Regression model in Python, right?
You’ve found the right Linear Regression course!
After completing this course you will be able to:
Identify the business problem which can be solved using linear and logistic regression technique of Machine Learning.
Create a linear regression and logistic regression model in Python and analyze its result.
Confidently model and solve regression and classification problems
A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.
What is covered in this course?
This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems.
Below are the course contents of this course on Linear Regression:
Section 1 – Basics of Statistics
This section is divided into five different lectures starting from types of data then types of statistics
then graphical representations to describe the data and then a lecture on measures of center like mean
median and mode and lastly measures of dispersion like range and standard deviation
Section 2 – Python basic
This section gets you started with Python.
This section will help you set up the python and Jupyter environment on your system and it’ll teach
you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.
Section 3 – Introduction to Machine Learning
In this section we will learn – What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.
Section 4 – Data Preprocessing
In this section you will learn what actions you need to take a step by step to get the data and then
prepare it for the analysis these steps are very important.
We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation.
Section 5 – Regression Model
This section starts with simple linear regression and then covers multiple linear regression.
We have covered the basic theory behind each concept without getting too mathematical about it so that you
understand where the concept is coming from and how it is important. But even if you don’t understand
it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.
We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem.
By the end of this course, your confidence in creating a regression model in Python will soar. You’ll have a thorough understanding of how to use regression modelling to create predictive models and solve business problems.

How this course will help you?
If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning, which is Linear Regression and Logistic Regregression
Why should you choose this course?
This course covers all the steps that one should take while solving a business problem through linear and logistic regression.
Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.
What makes us qualified to teach you?
The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course
We are also the creators of some of the most popular online courses – with over 150,000 enrollments and thousands of 5-star reviews like these ones:
This is very good, i love the fact the all explanation given can be understood by a layman – Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price. – Daisy
Our Promise
Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.
Download Practice files, take Quizzes, and complete Assignments
With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.

Go ahead and click the enroll button, and I’ll see you in lesson 1!

Cheers
Start-Tech Academy

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Below is a list of popular FAQs of students who want to start their Machine learning journey-
What is Machine Learning?
Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
What is the Linear regression technique of Machine learning?
Linear Regression is a simple machine learning model for regression problems, i.e., when the target variable is a real value.
Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the input variables (x).
When there is a single input variable (x), the method is referred to as simple linear regression.
When there are multiple input variables, the method is known as multiple linear regression.
Why learn Linear regression technique of Machine learning?
There are four reasons to learn Linear regression technique of Machine learning:
1. Linear Regression is the most popular machine learning technique
2. Linear Regression has fairly good prediction accuracy
3. Linear Regression is simple to implement and easy to interpret
4. It gives you a firm base to start learning other advanced techniques of Machine Learning
How much time does it take to learn Linear regression technique of machine learning?
Linear Regression is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn Linear regression starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to remember whatever you have learnt. Therefore, we have also provided you with another data set to work on as a separate project of Linear regression.
What are the steps I should follow to be able to build a Machine Learning model?
You can divide your learning process into 4 parts:
Statistics and Probability – Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.
Understanding of Machine learning – Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model
Programming Experience – A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in Python
Understanding of Linear and Logistic Regression modelling – Having a good knowledge of Linear and Logistic Regression gives you a solid understanding of how machine learning works. Even though Linear regression is the simplest technique of Machine learning, it is still the most popular one with fairly good prediction ability. Fifth and sixth section cover Linear regression topic end-to-end and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.
Why use Python for data Machine Learning?
Understanding Python is one of the valuable skills needed for a career in Machine Learning.
Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:
In 2016, it overtook R on Kaggle, the premier platform for data science competitions.
In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.
In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.
Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.

Who this course is for:
People pursuing a career in data science
Working Professionals beginning their Data journey
Statisticians needing more practical experience
Anyone curious to master Linear and Logistic Regression from beginner to advanced level in a short span of time

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