Created by Start-Tech Academy | Last updated 11/2019
Duration: 6 hours | Video: h264, 1280x720 | Audio: AAC, 44 KHz, 2 Ch | 2.6 GB
Genre: eLearning | Language: English + Sub | 75 lectures

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

Understand how to interpret the result of Logistic Regression model and translate them into actionable insight
Learn the linear discriminant analysis and K-Nearest Neighbors technique in R studio
Learn how to solve real life problem using the different classification techniques
Preliminary analysis of data using Univariate analysis before running classification model
Predict future outcomes basis past data by implementing Machine Learning algorithm
Indepth knowledge of data collection and data preprocessing for Machine Learning logistic regression problem
Course contains a end-to-end DIY project to implement your learnings from the lectures
Graphically representing data in R before and after analysis
How to do basic statistical operations in R
Students will need to install R and R studio software but we have a separate lecture to help you install the same

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 modelling model in R and compare their performance.
· Confidently practice, discuss and understand Machine Learning concepts
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How this course will help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.
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 Classification techniques of machine learning, such as Logistic Regression, Linear Discriminant Analysis and KNN
Why should you choose this 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 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.
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:

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