Simple regression and multiple regression | Must know for machine learning and econometrics | Linear regression in R Studio
You are looking for the perfect Linear regression course Which teaches you everything you need to build a linear regression model in R, right?
You’ve found the right linear regression course!
After completing this course, Dr. You will be able to:
खा Identify business problems that can be solved using machine learning linear regression techniques.
Create a linear regression model in R and analyze its result.
· Practice, discuss and understand machine learning concepts with confidence
A Verification certificate of completion This machine is introduced to all students doing the Basics of Learning Basics course.
How will this course help you?
If you are a business manager or executive, or a student who wants to learn and apply machine learning in real world problems in business, this course will give you a solid foundation by teaching you the most popular techniques of machine learning. Is linear regression
Why should you choose this course?
This course covers all the steps required to solve 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 the analysis is more important is that you have the right data before running the analysis and do some pre-processing on it. And after running the analysis, you should be able to determine how good your model is and explain the results to actually help your business.
What qualifies us to teach you?
This course is taught by Abhishek and Pukhraj. As a manager at a global analytics consulting firm, we have helped businesses solve their business problems using machine learning techniques, and we have used our experience to incorporate practical aspects of data analysis into this course.
With over 150,000 enrollments and thousands of 5-star reviews like this – we’re also the creators of some of the most popular online courses:
This is very good, I like that all the explanations given can be understood by the common man – Joshua
Thanks to the author for this wonderful course. You are the best and this course is worth it. – Daisy
Our word
It is our job to teach our students and we are committed to that. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post questions in the course or send us a direct message.
Download practice files, take quizzes and complete assignments
With each lecture, class notes are attached for you to follow. You can also take a quiz to understand your concept. Each section has a practice assignment to put your learning into practice.
What is included in this course?
This course teaches you all the steps to create a linear regression model, which is the most popular machine learning model, for solving business problems.
The content of this course on linear regression is given below:
A Section 1 – Statistics Basics
This section is divided into five different lectures, starting with the types of data and then into the types of statistics
Then a graphical presentation to describe the data and then a lecture on intermediate solutions
Measurements of mean and mode and finally dispersion such as range and standard deviation
A Section 2 – R Basic
This section will help you set up R and R Studio on your system and teach you how to perform some basic operations in R.
A Section 3 – Introduction to Machine Learning
In this section we will learn – what is machine learning. What are the meanings or different terms associated with machine learning? You will see some examples so that you can understand what machine learning is. It also includes the stages of making a machine learning model, not just a linear model but any machine learning model.
A Section 4 – Data preprocessing
In this section you will learn what actions you need to take to get the data and then
Prepare for analysis These steps are very important.
We start by understanding the importance of business knowledge and then let’s see how to do data exploration. We will learn how to do Uni-Variety Analysis and Buy-Variet Analysis, then we will cover the topic. External treatment, missing value charges, variable transformation and correlation.
A Section 5 – Regression Model
This section starts with simple linear regression and then covers multiple linear regression.
We have covered the basic principle behind each concept without much mathematics so that you can understand where the concept came from and how important it is. But even if you don’t understand it, it will work and as long as you learn how to interpret the result as taught in the practical lectures. We also look at how to quantify the accuracy of models, what F statistics mean, how clear variables in individual variables datasets are interpreted in results, what are the other differences in the general minimum class method, and how we finally interpret the result. To find the answer to a business problem.
At the end of this course, your confidence to build a regression model in R will increase. You will have a thorough knowledge of how to use regression modeling to create predictive models and solve business problems.
Go ahead and click on the Enrollment button and I’ll see you in Chapter 1!
Cheers
Start-Tech Academy
A
Below is a list of popular FAQs for students looking to embark on their machine learning journey-
What is machine learning?
Machine learning is a field of computer science that gives computers the ability to learn without explicitly programming. 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 the actual value.
Linear regression is a linear model, e.g. A model that assumes a linear relationship between input variables (x) and a single output variable (y). More specifically, y can be calculated from the linear combination of 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 techniques of machine learning?
There are four reasons to learn linear regression techniques of machine learning:
1. Linear regression is the most popular machine learning technique
2. Estimation accuracy is good in linear regression
3. Linear regression is easy to implement and easy to interpret
4. It gives you a strong foundation to start learning other advanced techniques of machine learning
How long does it take to learn the linear regression technique of machine learning?
Linear regression is easy but no one can determine the learning time required for it. It’s totally up to you. The method we adopted to help you learn linear regression starts with the basics and takes you to an advanced level in a matter of hours. You can follow it, but remember that you can’t learn anything without practicing. Practicing is the only way to remember what you learned. Therefore, we have also provided you with another data set to work as a separate project of linear regression.
What steps should I follow to be able to build a machine learning model?
You can divide your learning process into 4 parts:
Statistics and Probability – Basic knowledge of statistics and probability concepts is required to implement machine learning techniques. This part is included in the second section of the syllabus.
Understanding Machine Learning – Section 4 helps you to understand the terms and concepts related to machine learning and gives you the steps to follow to build a machine learning model.
Programming Experience – An important part of machine learning is programming. Python and R are clearly leaders in recent days. The third section will help you set up the R environment and teach you some basic operations. The later parts have a video of how to implement each of the concepts taught in the theory lecture in R.
Understanding Linear Regression Modeling – With a good knowledge of linear regression you get a solid understanding of how machine learning works. Although linear regression is the simplest technique of machine learning, it is still the most popular with good predictability. The fifth and sixth sections have an end-to-end cover of the topic of linear regression, and a corresponding practical lecture is given in R with each theory lecture where we run each query with us.
Why use R for data machine learning?
R Understanding is one of the most valuable skills required for a career in machine learning. Here are some reasons why you should learn machine learning in R.
1. It is a popular language for machine learning in top tech companies. Almost all data scientists hire people who use R. Facebook, for example, to use R to analyze behavior with a user’s post data. Uses Google R to evaluate advertising effectiveness and make financial predictions. And by the way, these are not just tech firms: R is used in analysis and consulting firms, banks and other financial institutions, educational institutions and research laboratories, and everywhere else analysis and visualizing of data is required.
2. Learning the basics of data science in R is undoubtedly easy. R has one major advantage: it is specifically designed with data handling and analysis in mind.
3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that is great for data science.
4. A strong, growing community of data scientists and statisticians. As the field of data science has evolved, R has exploded, becoming the fastest growing language in the world (as measured by stackoverflow). This means it’s easy to find answers to questions and community guidance as you work your way through projects in R.
5. Keep another tool in your toolkit. No single language will be the right tool for every task. Adding R to your store will make some projects easier – and of course, it will also make you a more flexible and marketable employee when you are looking for jobs in data science.
What is the difference between data mining, machine learning and deep learning?
Simply put, machine learning and data mining use the same algorithms and techniques as data mining, except that the estimates are different. While data mining seeks previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge — and further that information is automatically applied to data, decision making, and actions.
Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data for learning, understanding, and identifying complex patterns. Automatic language translation and medical diagnostics are examples of in-depth learning.
Simple regression and multiple regression | Must know for machine learning and econometrics | Linear regression in R Studio
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