## ML for Business Managers Build Regression model in R Studio

### What you’ll learn

Learn how to solve real life problem using the Linear Regression technique

Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression

Predict future outcomes basis past data by implementing Simplest Machine Learning algorithm

Understand how to interpret the result of Linear Regression model and translate them into actionable insight

Understanding of basics of statistics and concepts of Machine Learning

Indepth knowledge of data collection and data preprocessing for Machine Learning Linear Regression problem

Learn advanced variations of OLS method of Linear Regression

Course contains a end-to-end DIY project to implement your learnings from the lectures

How to convert business problem into a Machine learning Linear Regression problem

How to do basic statistical operations in R

Advanced Linear regression techniques using GLMNET package of R

Graphically representing data in R before and after analysis

### Requirements

Students will need to install R and R studio software but we have a separate lecture to help you install the same

### Description

You’re looking for a complete Linear Regression course that teaches you everything you need to create a Linear Regression model in R, right?You’ve found the right Linear Regression course!After completing this course you will be able to:· Identify the business problem which can be solved using linear regression technique of Machine Learning.· Create a linear regression model in R and analyze its result.· Confidently practice, discuss and understand Machine Learning conceptsA Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.How this course will help you?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 technique of machine learning, which is Linear RegressionWhy should you choose this course?This course covers all the steps that one should take while solving 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 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 courseWe 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 – JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. – DaisyOur PromiseTeaching 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 AssignmentsWith 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 StatisticsThis section is divided into five different lectures starting from types of data then types of statisticsthen graphical representations to describe the data and then a lecture on measures of center like meanmedian and mode and lastly measures of dispersion like range and standard deviation· Section 2 – R basicThis 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 LearningIn 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 PreprocessingIn this section you will learn what actions you need to take a step by step to get the data and thenprepare 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 uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation.· Section 5 – Regression ModelThis section starts with simple linear regression and then covers multiple linear regression.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 accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method 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 regression model in R will soar. You’ll have a thorough understanding of how to use regression modelling to create predictive models and solve business problems.Go ahead and click the enroll button, and I’ll see you in lesson 1!CheersStart-Tech Academy————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 which 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.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 a real value.Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the 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 technique of Machine learning?There are four reasons to learn Linear regression technique of Machine learning:1. Linear Regression is the most popular machine learning technique2. Linear Regression has fairly good prediction accuracy3. Linear Regression is simple to implement and easy to interpret4. It gives you a firm base to start learning other advanced techniques of Machine LearningHow much time does it take to learn Linear regression technique of machine learning?Linear Regression 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 Linear regression starts from the basics and takes you to 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 learnt. Therefore, we have also provided you with another data set to work on as a separate project of Linear regression.What are the steps I should follow to be able to build a Machine Learning model?You can divide your learning process into 4 parts:Statistics and Probability – Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.Understanding of Machine learning – 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 modelProgramming Experience – A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the R environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in RUnderstanding of Linear Regression modelling – Having a good knowledge of Linear Regression gives you a solid understanding of how machine learning works. Even though Linear regression is the simplest technique of Machine learning, it is still the most popular one with fairly good prediction ability. Fifth and sixth section cover Linear regression topic end-to-end and with each theory lecture comes a corresponding practical lecture in R where we actually run each query with you.Why use R for data 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 and 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.What is the difference between Data Mining, Machine Learning, and Deep Learning?Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information 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 to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.

### Overview

Section 1: Introduction

Lecture 1 Welcome to the course!

Lecture 2 Course Resources

Lecture 3 Course contents

Lecture 4 This is a milestone!

Section 2: Basics of Statistics

Lecture 5 Types of Data

Lecture 6 Types of Statistics

Lecture 7 Describing the data graphically

Lecture 8 Measures of Centers

Lecture 9 Practice Exercise 1

Lecture 10 Measures of Dispersion

Lecture 11 Practice Exercise 2

Section 3: Getting started with R and R studio

Lecture 12 Installing R and R studio

Lecture 13 Basics of R and R studio

Lecture 14 Packages in R

Lecture 15 Inputting data part 1: Inbuilt datasets of R

Lecture 16 Inputting data part 2: Manual data entry

Lecture 17 Inputting data part 3: Importing from CSV or Text files

Lecture 18 Creating Barplots in R

Lecture 19 Creating Histograms in R

Section 4: Introduction to Machine Learning

Lecture 20 Introduction to Machine Learning

Lecture 21 Building a Machine Learning model

Section 5: Data Preprocessing

Lecture 22 Gathering Business Knowledge

Lecture 23 Data Exploration

Lecture 24 The Data and the Data Dictionary

Lecture 25 Importing the dataset into R

Lecture 26 Project Exercise 1

Lecture 27 Univariate Analysis and EDD

Lecture 28 EDD in R

Lecture 29 Project Exercise 2

Lecture 30 Outlier Treatment

Lecture 31 Outlier Treatment in R

Lecture 32 Project Exercise 3

Lecture 33 Missing Value imputation

Lecture 34 Missing Value imputation in R

Lecture 35 Project Exercise 4

Lecture 36 Seasonality in Data

Lecture 37 Bi-variate Analysis and Variable Transformation

Lecture 38 Variable transformation in R

Lecture 39 Project Exercise 5

Lecture 40 Non Usable Variables

Lecture 41 Dummy variable creation: Handling qualitative data

Lecture 42 Dummy variable creation in R

Lecture 43 Project Exercise 6

Lecture 44 Correlation Matrix and cause-effect relationship

Lecture 45 Correlation Matrix in R

Lecture 46 Project Exercise 7

Section 6: Linear Regression Model

Lecture 47 The problem statement

Lecture 48 Basic equations and Ordinary Least Squared (OLS) method

Lecture 49 Assessing Accuracy of predicted coefficients

Lecture 50 Assessing Model Accuracy – RSE and R squared

Lecture 51 Simple Linear Regression in R

Lecture 52 Project Exercise 8

Lecture 53 Multiple Linear Regression

Lecture 54 The F – statistic

Lecture 55 Interpreting result for categorical Variable

Lecture 56 Multiple Linear Regression in R

Lecture 57 Project Exercise 9

Lecture 58 Test-Train split

Lecture 59 Bias Variance trade-off

Lecture 60 More about test-train split

Lecture 61 Test-Train Split in R

Section 7: Regression models other than OLS

Lecture 62 Linear models other than OLS

Lecture 63 Subset Selection techniques

Lecture 64 Subset selection in R

Lecture 65 Project Exercise 10

Lecture 66 Shrinkage methods – Ridge Regression and The Lasso

Lecture 67 Ridge regression and Lasso in R

Lecture 68 Heteroscedasticity

Lecture 69 Project Exercise 11

Section 8: Conclusion

Lecture 70 Final Project Exercise

Lecture 71 The final milestone!

Lecture 72 Bonus lecture

People pursuing a career in data science,Working Professionals beginning their Data journey,Statisticians needing more practical experience,Anyone curious to master Linear Regression from beginner to advanced in short span of time

#### Course Information:

Udemy | English | 6h 21m | 2.92 GB

Created by: Start-Tech Academy

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