Complete Machine Learning with R Studio ML for 2023

Linear & Logistic Regression, Decision Trees, XGBoost, SVM & other ML models in R programming language – R studio
Complete Machine Learning with R Studio ML for 2023
File Size :
4.07 GB
Total length :
11h 58m



Start-Tech Academy


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Complete Machine Learning with R Studio ML for 2023

What you’ll 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

Complete Machine Learning with R Studio ML for 2023


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 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 competitionsCheck 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 – 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, 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 AssignmentsWith 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 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 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 PythonUnderstanding 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 R1. 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 PackagesPython has main packages for data analysis tasks, R has a larger ecosystem of small packagesGraphics capabilities are generally considered better in R than in PythonR has more statistical support in general than PythonWhat 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.


Section 1: Welcome to the course

Lecture 1 Introduction

Lecture 2 Course Resources

Section 2: Setting up R Studio and R crash course

Lecture 3 Installing R and R studio

Lecture 4 This is a milestone!

Lecture 5 Basics of R and R studio

Lecture 6 Packages in R

Lecture 7 Inputting data part 1: Inbuilt datasets of R

Lecture 8 Inputting data part 2: Manual data entry

Lecture 9 Inputting data part 3: Importing from CSV or Text files

Lecture 10 Creating Barplots in R

Lecture 11 Creating Histograms in R

Section 3: Basics of Statistics

Lecture 12 Types of Data

Lecture 13 Types of Statistics

Lecture 14 Describing the data graphically

Lecture 15 Measures of Centers

Lecture 16 Measures of Dispersion

Section 4: Intorduction to Machine Learning

Lecture 17 Introduction to Machine Learning

Lecture 18 Building a Machine Learning Model

Section 5: Data Preprocessing for Regression Analysis

Lecture 19 Gathering Business Knowledge

Lecture 20 Data Exploration

Lecture 21 The Data and the Data Dictionary

Lecture 22 Importing the dataset into R

Lecture 23 Univariate Analysis and EDD

Lecture 24 EDD in R

Lecture 25 Outlier Treatment

Lecture 26 Outlier Treatment in R

Lecture 27 Missing Value imputation

Lecture 28 Missing Value imputation in R

Lecture 29 Seasonality in Data

Lecture 30 Bi-variate Analysis and Variable Transformation

Lecture 31 Variable transformation in R

Lecture 32 Non Usable Variables

Lecture 33 Dummy variable creation: Handling qualitative data

Lecture 34 Dummy variable creation in R

Lecture 35 Correlation Matrix and cause-effect relationship

Lecture 36 Correlation Matrix in R

Section 6: Linear Regression Model

Lecture 37 The problem statement

Lecture 38 Basic equations and Ordinary Least Squared (OLS) method

Lecture 39 Assessing Accuracy of predicted coefficients

Lecture 40 Assessing Model Accuracy – RSE and R squared

Lecture 41 Simple Linear Regression in R

Lecture 42 Multiple Linear Regression

Lecture 43 The F – statistic

Lecture 44 Interpreting result for categorical Variable

Lecture 45 Multiple Linear Regression in R

Lecture 46 Test-Train split

Lecture 47 Bias Variance trade-off

Lecture 48 More about test-train split

Lecture 49 Test-Train Split in R

Section 7: Regression models other than OLS

Lecture 50 Linear models other than OLS

Lecture 51 Subset Selection techniques

Lecture 52 Subset selection in R

Lecture 53 Shrinkage methods – Ridge Regression and The Lasso

Lecture 54 Ridge regression and Lasso in R

Section 8: Introduction to the classification Models

Lecture 55 Three classification models and Data set

Lecture 56 Importing the data into R

Lecture 57 The problem statements

Lecture 58 Why can’t we use Linear Regression?

Section 9: Logistic Regression

Lecture 59 Logistic Regression

Lecture 60 Training a Simple Logistic model in R

Lecture 61 Results of Simple Logistic Regression

Lecture 62 Logistic with multiple predictors

Lecture 63 Training multiple predictor Logistic model in R

Lecture 64 Confusion Matrix

Lecture 65 Evaluating Model performance

Lecture 66 Predicting probabilities, assigning classes and making Confusion Matrix in R

Section 10: Linear Discriminant Analysis

Lecture 67 Linear Discriminant Analysis

Lecture 68 Linear Discriminant Analysis in R

Section 11: K-Nearest Neighbors

Lecture 69 Test-Train Split

Lecture 70 Test-Train Split in R

Lecture 71 K-Nearest Neighbors classifier

Lecture 72 K-Nearest Neighbors in R

Section 12: Comparing results from 3 models

Lecture 73 Understanding the results of classification models

Lecture 74 Summary of the three models

Section 13: Simple Decision Trees

Lecture 75 Introduction to Decision trees

Lecture 76 Basics of Decision Trees

Lecture 77 Understanding a Regression Tree

Lecture 78 The stopping criteria for controlling tree growth

Lecture 79 Course resources: Notes and Datasets

Lecture 80 Importing the Data set into R

Lecture 81 Splitting Data into Test and Train Set in R

Lecture 82 Building a Regression Tree in R

Lecture 83 Pruning a tree

Lecture 84 Pruning a Tree in R

Section 14: Simple Classification Tree

Lecture 85 Classification Trees

Lecture 86 The Data set for Classification problem

Lecture 87 Building a classification Tree in R

Lecture 88 Advantages and Disadvantages of Decision Trees

Section 15: Ensemble technique 1 – Bagging

Lecture 89 Bagging

Lecture 90 Bagging in R

Section 16: Ensemble technique 2 – Random Forest

Lecture 91 Random Forest technique

Lecture 92 Random Forest in R

Section 17: Ensemble technique 3 – GBM, AdaBoost and XGBoost

Lecture 93 Boosting techniques

Lecture 94 Gradient Boosting in R

Lecture 95 AdaBoosting in R

Lecture 96 XGBoosting in R

Section 18: Support Vector Machines

Lecture 97 Introduction to SVM

Lecture 98 The Concept of a Hyperplane

Lecture 99 Maximum Margin Classifier

Lecture 100 Limitations of Maximum Margin Classifier

Section 19: Support Vector Classifier

Lecture 101 Support Vector classifiers

Lecture 102 Limitations of Support Vector Classifiers

Section 20: Support Vector Machines

Lecture 103 Kernel Based Support Vector Machines

Section 21: Creating Support Vector Machine Model in R

Lecture 104 Course resources: Notes and Datasets

Lecture 105 Importing and preprocessing data

Lecture 106 Classification SVM model using Linear Kernel

Lecture 107 Hyperparameter Tuning for Linear Kernel

Lecture 108 Polynomial Kernel with Hyperparameter Tuning

Lecture 109 Radial Kernel with Hyperparameter Tuning

Lecture 110 SVM based Regression Model in R

Section 22: Congratulations & about your certificate

Lecture 111 The final milestone!

Lecture 112 Bonus Lecture

People pursuing a career in data science,Working Professionals beginning their Data journey,Statisticians needing more practical experience

Course Information:

Udemy | English | 11h 58m | 4.07 GB
Created by: Start-Tech Academy

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