R Studio Logistic Regression Course: Affordable and Effective

Affordable and Effective Logistic Regression Course

Start-Tech Academy

Last Updated on December 21, 2023 by GeeksGod

Course : Logistic Regression in R Studio

Free Udemy Coupon: Boost Your Skills with Logistic Regression Course

You’re looking for a complete Classification modeling course that teaches you everything you need to create a Classification model in R, right?

You’ve found the right Classification modeling course covering logistic regression, LDA, and kNN in R studio!

After completing this course, you will be able to:

  • Identify the business problem which can be solved using Classification modeling techniques of Machine Learning.
  • Create different Classification modeling models in R and compare their performance.
  • Confidently practice, discuss, and understand Machine Learning concepts

Why Take This Free Udemy Coupon Logistic Regression Course?

This course covers all the steps that one should take while solving a business problem using classification techniques.

Most courses only focus on teaching how to run the analysis, but we believe that what happens before and after running the analysis is even more important, i.e. before running the analysis, it is very important that you have the right data and do some pre-processing on it. And after running the 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 problems 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.

What is covered in this course?

This course teaches you all the steps of creating a classification model to solve business problems.

Below are the course contents of this course on Logistic Regression:

  • Section 1 – Basics of Statistics
  • Section 2 – R Basic
  • Section 3 – Introduction to Machine Learning
  • Section 4 – Data Pre-processing
  • Section 5 – Classification Models

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’ performance using a confusion matrix, how categorical variables in the independent variables dataset are interpreted in the results, test-train split, 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 classification model in R will soar. You’ll have a thorough understanding of how to use Classification modeling to create predictive models and solve business problems.

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


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Frequently Asked Questions (FAQs) about Logistic Regression Course:

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 that 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.

Which all classification techniques are taught in this course?

In this course, we learn both parametric and non-parametric classification techniques. The primary focus will be on the following three techniques:

  • Logistic Regression
  • Linear Discriminant Analysis
  • K – Nearest Neighbors (KNN)

How much time does it take to learn Classification techniques of machine learning?

Classification 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 classification starts from the basics and takes you to an 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 learned. Therefore, we have also provided you with another dataset to work on as a separate project of classification.

What are the steps I should follow to be able to build a Machine Learning model?

You can divide your learning process into 3 parts:

  1. Statistics and Probability – Implementing Machine learning techniques require a basic knowledge of Statistics and probability concepts. The second section of the course covers this part.
  2. Understanding of Machine learning – The 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
  3. Programming Experience – A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. The 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 lectures in Python

Understanding of models – Fifth and sixth section cover Classification models and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.

Why use R for Machine Learning?

Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Machine learning in R:

  1. It’s a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions, research labs, and pretty much everywhere else data needs analyzing and visualizing.
  2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation 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 are great for data science.
  4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means it’s easy to find answers to questions and community guidance as you work your way through projects in R.
  5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science.

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Udemy Coupon :


What you will learn :

1. Understand how to interpret the result of Logistic Regression model and translate them into actionable insight
2. Learn the linear discriminant analysis and K-Nearest Neighbors technique in R studio
3. Learn how to solve real life problem using the different classification techniques
4. Preliminary analysis of data using Univariate analysis before running classification model
5. Predict future outcomes basis past data by implementing Machine Learning algorithm
6. Indepth knowledge of data collection and data preprocessing for Machine Learning logistic regression problem
7. Course contains a end-to-end DIY project to implement your learnings from the lectures
8. Graphically representing data in R before and after analysis
9. How to do basic statistical operations in R

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