Start Machine Learning Here: Linear Regression model in R faq

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Learn how to solve real-world problems using the Linear Regression technique. Get an in-depth understanding of data collection and preprocessing for Machine Learning Linear Regression problems. Discover advanced variations of the OLS method of Linear Regression. This course contains an end-to-end DIY project to implement your learnings from the lectures. Start your Machine Learning journey here!

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Course Feature

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Udemy

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English

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Self Paced

Course Overview

❗The content presented here is sourced directly from Udemy platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [June 30th, 2023]

This course provides an introduction to the Linear Regression technique and its application in solving real-life problems. Participants will learn how to perform preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. They will also learn how to predict future outcomes based on past data by implementing the simplest Machine Learning algorithm. Additionally, participants will gain an understanding of the basics of statistics and concepts of Machine Learning, as well as an in-depth knowledge of data collection and data preprocessing for Machine Learning Linear Regression problems. Advanced variations of the OLS method of Linear Regression will also be discussed. The course includes a hands-on project to implement the learnings from the lectures, as well as instruction on how to convert business problems into Machine Learning Linear Regression problems, how to do basic statistical operations in R, and how to graphically represent data in R before and after analysis.

[Applications]
After this course, learners can apply the knowledge gained to solve real-life problems using the Linear Regression technique. They can use Univariate and Bivariate analysis to do preliminary analysis of data before running Linear regression. Learners can also use the Simplest Machine Learning algorithm to predict future outcomes basis past data. They can interpret the result of Linear Regression model and translate them into actionable insight. Learners can also understand the basics of statistics and concepts of Machine Learning, as well as data collection and data preprocessing for Machine Learning Linear Regression problem. They can also learn advanced variations of OLS method of Linear Regression, and use the end-to-end DIY project to implement their learnings from the lectures. Learners can also learn how to convert business problem into a Machine learning Linear Regression problem, and how to do basic statistical operations in R, as well as advanced Linear regression techniques using GLMNET package of R. Finally, learners can also learn how to graphically represent data in R before and after analysis.

[Career Paths]
A career path recommended to learners of this course is a Machine Learning Engineer. A Machine Learning Engineer is responsible for developing and deploying machine learning models and algorithms to solve real-world problems. They are also responsible for designing, building, and maintaining machine learning systems. They must have a strong understanding of data science, statistics, and programming. They must also be able to interpret and analyze data to identify patterns and trends.

The development trend of Machine Learning Engineers is increasing rapidly as more and more companies are looking to leverage the power of machine learning to improve their products and services. Companies are investing heavily in machine learning engineers to develop and deploy machine learning models and algorithms. As the demand for machine learning engineers increases, so does the need for more advanced skills and knowledge. Companies are looking for engineers who can develop and deploy models quickly and efficiently, as well as those who can interpret and analyze data to identify patterns and trends.

[Education Paths]
The recommended educational path for learners of this course is to pursue a degree in Data Science or Machine Learning. This degree will provide learners with the necessary skills and knowledge to understand and apply the concepts of Machine Learning and Linear Regression. The degree will cover topics such as data collection and preprocessing, statistical operations, advanced linear regression techniques, and graphical representation of data. Additionally, the degree will provide learners with the opportunity to develop their own projects and apply their knowledge to real-world problems.

The development trend of this degree is to focus on the application of Machine Learning and Linear Regression to solve real-world problems. This includes the use of advanced techniques such as GLMNET and the development of projects that can be used to demonstrate the knowledge and skills acquired. Additionally, the degree will focus on the development of data-driven decision-making skills, which are essential for the successful application of Machine Learning and Linear Regression.

Pros & Cons

Pros Cons
  • pros

    Well-explained concepts and coding examples.

  • pros

    Hands-on practice for better understanding.

  • pros

    Suitable for Business Managers.

  • pros

    Profound and insightful.

  • pros

    Suitable for beginners in programming.

  • pros

    Informative.

  • pros

    Great understanding and efficient explanations.

  • cons

    Theoretical part needs more subtle explanations.

  • cons

    None mentioned.

Course Provider

Provider Udemy's Stats at AZClass

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