Machine Learning for Beginners: Linear Regression model in R Course Catalog
Simple Regression & Multiple Regression| must-know for Machine Learning & Econometrics | Linear Regression in R studio
What you’ll learn
Machine Learning for Beginners: Linear Regression model in R Course Catalog
- Learn how to solve the 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 the Simplest Machine Learning algorithm
- Understand how to interpret the result of a Linear Regression model and translate them into actionable insight
- Understanding of basics of statistics and concepts of Machine Learning
- In-depth knowledge of data collection and data preprocessing for Machine Learning Linear Regression problem
- Learn advanced variations of the OLS method of Linear Regression
- The course contains an end-to-end DIY project to implement your learnings from the lectures
- How to convert the 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
- Students will need to install R and R studio software but we have a separate lecture to help you install the same
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:
· Create a linear regression model in R and analyze its result.
· Confidently practice, discuss and understand Machine Learning concepts
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.
Below are the course contents of this course on Linear Regression:
· Section 1 – Basics of Statistics
then graphical representations to describe the data and then a lecture on measures of a 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. Machine Learning for Beginners
· 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 a 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.
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 modeling to create predictive models and solve business problems.
Below is a list of popular FAQs of students who want to start their Machine learning journey-
What is Machine Learning?
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).
Why learn the 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 a 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 – Machine Learning for Beginners
How much time does it take to learn the 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 an advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it.
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. The second section of the course covers this part.
Programming Experience – A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in recent days. The 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 modeling – 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.
The fifth and sixth section covers 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. – Machine Learning for Beginners
Why use R for data 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.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. A 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.
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 like 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.
Who this course is for:
- 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 a short span of time
- Data Science and Machine Learning Bootcamp with R
- Last updated 3/2020