ML for Business Managers: Build Regression model in R Studio
ML for Business Managers: Build Regression model in R Studio.
Simple Regression & Multiple Regression| must-know for Machine Learning
& Econometrics | Linear Regression in R studio.
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 concepts
A 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 Regression
Why 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 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:
· 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 uni-variate analysis and bi-variate
analysis then we cover topics like outlier treatment, missing value imputation, variable
transformation and correlation.
· Section 5 – Regression Model
This 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!
Cheers
Start-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 technique
2. Linear Regression
has fairly good prediction accuracy
3. Linear Regression
is simple to implement and easy to interpret
4. It gives you a
firm base to start learning other advanced techniques of Machine Learning
How 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 model
Programming 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 R
Understanding 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.
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