Complete Machine Learning with R Studio – ML for 2022

Linear & Logistic Regression, Decision Trees, XGBoost, SVM & other ML models in R programming language – R studio

What you will learn

☑Learn how to solve real life problem using the Machine learning techniques

☑Machine Learning models such as Linear Regression, Logistic Regression, KNN etc.

☑Advanced Machine Learning models such as Decision trees, XGBoost, Random Forest, SVM etc.

☑Understanding of basics of statistics and concepts of Machine Learning

☑How to do basic statistical operations and run ML models in R

☑Indepth knowledge of data collection and data preprocessing for Machine Learning problem

☑How to convert business problem into a Machine learning problem


You’re looking for a complete Machine Learning course that can help you launch a flourishing career in the field of Data Science, Machine Learning, R and Predictive Modeling, right?

You’ve found the right Machine Learning course!

After completing this course, you will be able to:

· Confidently build predictive Machine Learning models using R to solve business problems and create business strategy

· Answer Machine Learning related interview questions

· Participate and perform in online Data Analytics competitions such as Kaggle competitions

Check out the table of contents below to see what all Machine Learning models you are going to learn.

How will this course 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, R and predictive modelling in Real world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning, R and predictive modelling.

Why should you choose this course?

This course covers all the steps that one should take while solving a business problem through linear regression. This course will give you an in-depth understanding of machine learning and predictive modelling techniques using R.

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 using R, Python, 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, machine learning, R, predictive modelling, 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 of machine learning, R and predictive modelling. Each section contains a practice assignment for you to practically implement your learning on machine learning, R and predictive modelling.

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 are the steps I should follow to be able to build a Machine Learning model?

You can divide your learning process into 3 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 Python 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 Python

Understanding of models – Fifth and sixth section cover Classification models and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.

Why use R for 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 than Python. R has a big advantage: it was designed specifically with data manipulation and analysis in mind.

3. Amazing packages that make your life easier. As compared to Python, 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, usage of R and Python 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. Like Python, 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 are the major advantages of using R over Python?

  • As compared to Python, R has a higher user base and the biggest number of statistical packages and libraries available. Although, Python has almost all features that analysts need, R triumphs over Python.
  • R is a function-based language, whereas Python is object-oriented. If you are coming from a purely statistical background and are not looking to take over major software engineering tasks when productizing your models, R is an easier option, than Python.
  • R has more data analysis functionality built-in than Python, whereas Python relies on Packages
  • Python has main packages for data analysis tasks, R has a larger ecosystem of small packages
  • Graphics capabilities are generally considered better in R than in Python
  • R has more statistical support in general than Python

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.




Welcome to the course


Course resources: Notes and Datasets (Part 1)

Setting up R Studio and R crash course

Installing R and R studio

Basics of R and R studio

Packages in R

Inputting data part 1: Inbuilt datasets of R

Inputting data part 2: Manual data entry

Inputting data part 3: Importing from CSV or Text files

Creating Barplots in R

Creating Histograms in R

Basics of Statistics

Types of Data

Types of Statistics

Describing the data graphically

Measures of Centers

Measures of Dispersion

Intorduction to Machine Learning

Introduction to Machine Learning

Building a Machine Learning Model

Quiz: Introduction to Machine Learning

Data Preprocessing for Regression Analysis

Gathering Business Knowledge

Data Exploration

The Data and the Data Dictionary

Importing the dataset into R

Univariate Analysis and EDD

EDD in R

Outlier Treatment

Outlier Treatment in R

Missing Value imputation

Missing Value imputation in R

Seasonality in Data

Bi-variate Analysis and Variable Transformation

Variable transformation in R

Non Usable Variables

Dummy variable creation: Handling qualitative data

Dummy variable creation in R

Correlation Matrix and cause-effect relationship

Correlation Matrix in R

Linear Regression Model

The problem statement

Basic equations and Ordinary Least Squared (OLS) method

Assessing Accuracy of predicted coefficients

Assessing Model Accuracy – RSE and R squared

Simple Linear Regression in R

Multiple Linear Regression

The F – statistic

Interpreting result for categorical Variable

Multiple Linear Regression in R


Test-Train split

Bias Variance trade-off

Test-Train Split in R

Regression models other than OLS

Linear models other than OLS

Subset Selection techniques

Subset selection in R

Shrinkage methods – Ridge Regression and The Lasso

Ridge regression and Lasso in R

Classification Models: Data Preparation

The Data and the Data Dictionary

Course resources: Notes and Datasets

Importing the dataset into R

EDD in R

Outlier Treatment in R

Missing Value imputation in R

Variable transformation in R

Dummy variable creation in R

The Three classification models

Three Classifiers and the problem statement

Why can’t we use Linear Regression?

Logistic Regression

Logistic Regression

Training a Simple Logistic model in R

Results of Simple Logistic Regression

Logistic with multiple predictors

Training multiple predictor Logistic model in R

Confusion Matrix

Evaluating Model performance

Predicting probabilities, assigning classes and making Confusion Matrix

Linear Discriminant Analysis

Linear Discriminant Analysis

Linear Discriminant Analysis in R

K-Nearest Neighbors

Test-Train Split

Test-Train Split in R

K-Nearest Neighbors classifier

K-Nearest Neighbors in R

Comparing results from 3 models

Understanding the results of classification models

Summary of the three models

Simple Decision Trees

Basics of Decision Trees

Understanding a Regression Tree

The stopping criteria for controlling tree growth

The Data set for this part

Course resources: Notes and Datasets

Importing the Data set into R

Splitting Data into Test and Train Set in R

Building a Regression Tree in R

Pruning a tree

Pruning a Tree in R

Simple Classification Tree

Classification Trees

The Data set for Classification problem

Building a classification Tree in R

Advantages and Disadvantages of Decision Trees

Ensemble technique 1 – Bagging


Bagging in R

Ensemble technique 2 – Random Forest

Random Forest technique

Random Forest in R

Ensemble technique 3 – GBM, AdaBoost and XGBoost

Boosting techniques

Gradient Boosting in R

AdaBoosting in R

XGBoosting in R

Maximum Margin Classifier

Content flow

The Concept of a Hyperplane

Maximum Margin Classifier

Limitations of Maximum Margin Classifier

Support Vector Classifier

Support Vector classifiers

Limitations of Support Vector Classifiers

Support Vector Machines

Kernel Based Support Vector Machines

Creating Support Vector Machine Model in R

The Data set for the Classification problem

Course resources: Notes and Datasets

Importing Data into R

Test-Train Split

Classification SVM model using Linear Kernel

Hyperparameter Tuning for Linear Kernel

Polynomial Kernel with Hyperparameter Tuning

Radial Kernel with Hyperparameter Tuning

The Data set for the Regression problem

SVM based Regression Model in R


Course Conclusion

Bonus Lecture

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