Course description
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 modelling model in R and compare their performance.
· Confidently practice, discuss and understand Machine Learning concepts
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 preprocessing 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 5star 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:
· Section 1  Basics of Statistics
This section is divided into five different lectures starting from types of data then types of statistics then graphical representations to describe the data and then a lecture on measures of center like mean median and mode and lastly measures of dispersion like range and standard deviation
· Section 2  R basic
This section will help you set up the R and R studio on your system and it'll teach you how to perform some basic operations in R.
· Section 3  Introduction to Machine Learning
In 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 Preprocessing
In 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 univariate analysis and bivariate analysis then we cover topics like outlier treatment and missing value imputation.
· Section 5  Classification Models
This section starts with Logistic regression and then covers Linear Discriminant Analysis and KNearest Neighbors.
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 confusion matrix, how categorical variables in the independent variables dataset are interpreted in the results, testtrain 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!
Cheers
StartTech Academy
Requirements:
 Students will need to install R and R studio software but we have a separate lecture to help you install the same
What you'll learn:
 Understand how to interpret the result of Logistic Regression model and translate them into actionable insight
 Learn the linear discriminant analysis and KNearest 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 endtoend 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
Course curriculum

1
Introduction
 001 Welcome to the course FREE PREVIEW
 002 Introduction to Machine Learning
 003 Building a Machine Learning model
 Course Resources

2
Basics of Statistics
 004 Types of Data
 005 Types of Statistics
 006 Describing data Graphically
 007 Measures of Centers
 009 Measures of Dispersion

3
Setting up R and R crash course
 011 Installing R and R studio
 012 Basics of R and R studio
 013 Packages in R
 014 Inputting data part 1 Inbuilt datasets of R
 015 Inputting data part 2 Manual data entry
 016 Inputting data part 3 Importing from CSV or Text files
 017 Creating Barplots in R
 018 Creating Histograms in R

4
Data preparation and Preprocessing
 019 Gathering Business Knowledge
 020 Data Exploration
 021 Importing the dataset into R
 022 The Dataset and the Data Dictionary
 024 Univariate analysis and EDD
 025 EDD in R
 027 Outlier Treatment
 028 Outlier Treatment in R
 030 Missing Value Imputation
 031 Missing Value imputation in R
 033 Seasonality in Data
 034 Variable transformation in R
 036 Dummy variable creation Handling qualitative data
 037 Dummy variable creation in R

5
Classification models  Logistic Regression
 039 Three Classifiers and the problem statement
 040 Why cant we use Linear Regression
 041 Logistic Regression
 042 Training a Simple Logistic model in R
 044 Results of Simple Logistic Regression
 045 Logistic with multiple predictors
 046 Training multiple predictor Logistic model in R
 048 Confusion Matrix
 049 Evaluating Model performance
 050 Predicting probabilities assigning classes and making Confusion Matrix

6
LDA and KNN (KNearest Neighbors)
 052 Linear Discriminant Analysis
 053 Linear Discriminant Analysis in R
 055 TestTrain Split
 056 TestTrain Split in R
 058 KNearest Neighbors classifier
 059 KNearest Neighbors in R

7
Final Discussion
 061 Understanding the results of classification models
 062 Summary of the three models
Meet your instructor!
StartTech Academy
A technologybased analytics education company
Founded by Abhishek Bansal and Pukhraj Parikh, StartTech Academy is a technologybased analytics education company and aims at bringing together the analytics companies and interested Learners.
Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey.
Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python.
Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence.
Take this course today!
"Logistic Regression, LDA & KNN in R: Machine Learning models"

$27.81
Lifetime enrollment