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所在平台: Udemy |
课程主页: https://www.udemy.com/course/linear-regression-analysis-using-python-hindi/
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课程名称:机器学习基础 - 回归分析 课程概述:本课程旨在教授学员如何在Python中构建线性回归模型,解决商业问题。完成本课程后,学员将能够识别可以通过线性回归技术解决的商业问题,创建线性回归模型并分析其结果,充分理解并讨论机器学习的概念。所有参加此机器学习基础课程的学员将获得可验证的结业证书。 课程帮助:无论您是商业经理、高管还是希望将机器学习应用于实际商业问题的学生,本课程都能为您提供坚实的基础,教授机器学习中最流行的技术——线性回归。 选择本课程的原因:本课程涵盖了解决商业问题所需的所有步骤。大多数课程只关注分析的运行,而我们认为,正确的数据和预处理在分析前后都极为重要。课程教授如何评判模型的优劣及如何解释结果,以真正帮助商业决策。 授课师资:授课由全球分析咨询公司的经理Abhishek和Pukhraj主讲。他们在利用机器学习技术帮助企业解决问题方面积累了丰富的经验,并将实践内容融入课程中。 课程内容: 1. 统计学基础:讲解数据类型、统计类型、数据图形表示、中心趋势的度量(均值、中位数、众数)以及离散程度的度量(范围和标准差)。 2. Python基础:指导学员搭建Python和Jupyter环境,并介绍Numpy、Pandas和Seaborn等库的基本操作。 3. 机器学习简介:解释机器学习的含义及相关术语,展示机器学习的实际例子,并介绍构建机器学习模型的步骤。 4. 数据预处理:教导学员如何获取并准备数据以进行分析,包括数据探索、一元分析和二元分析,以及异常值处理和缺失值填补等主题。 5. 回归模型:从简单线性回归开始,逐步深入到多元线性回归,讲解各概念的基本理论,以及模型的准确性评估和结果解释等。 课程结尾,您将能够自信地在Python中创建回归模型,对使用回归建模进行预测并解决商业问题有深入理解。欢迎点击注册按钮,期待在第一节课见到您!
आप एक पूर्ण Linear Regression course की तलाश कर रहे हैं जो आपको वह सब कुछ सिखाता है जो आपको Python में Linear Regression model बनाने के लिए चाहिए, है ना?आपको सही Linear Regression course मिल गया है!After completing this course you will be able to:Identify the business problem which can be solved using linear regression technique of Machine Learning.Create a linear regression model in Python and analyze its result.Confidently practice, discuss and understand Machine Learning conceptsA Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.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 technique of machine learning, which is Linear RegressionWhy should you choose this course?This course covers all the steps that one should take while solving a business problem through linear 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 - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching 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 AssignmentsWith 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. 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 StatisticsThis section is divided into five different lectures starting from types of data then types of statisticsthen graphical representations to describe the data and then a lecture on measures of center like meanmedian and mode and lastly measures of dispersion like range and standard deviationSection 2 - Python basicThis section gets you started with Python.This section will help you set up the python and Jupyter environment on your system and it'll teachyou 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 LearningIn 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 PreprocessingIn 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 ModelThis 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.Go ahead and click the enroll button, and I'll see you in lesson 1!CheersStart-Tech AcademyBelow 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 technique2. Linear Regression has fairly good prediction accuracy3. Linear Regression is simple to implement and easy to interpret4. It gives you a firm base to start learning other advanced techniques of Machine LearningHow 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.