Machine Learning Made Easy - Beginner to Advanced using R

Machine Learning Made Easy : Beginner to Advanced using R, available at $54.99, has an average rating of 4.58, with 129 lectures, 11 quizzes, based on 103 reviews, and has 4396 subscribers.

You will learn about R Programming, Data Handling and Cleaning, Basic Statistics, Classical Machine Learning Algorithms, Model Selection and Validation, Advanced Machine Learning Algorithms, Ensemble Learning. Write your own R scripts and work in R environment. Import, manipulate, clean up, sanitize and export datasets. Understand basic statistics and implement using R. Understand data science life cycle while understanding steps of building, validating, improving and implementing the machine learning models. Do powerful analysis on data, find insights and present them in visual manner. Learn classical algorithms like Linear Regression, Logistic Regression, Decision Trees and advance machine learning algorithms like SVM, Artificial Neural Networks, Reinforced Learning, Random Forests and Boosting and clustering algorithms like K-means. Know how each machine learning algorithm works and which one to choose according to the type of problem. Build more than one powerful machine learning model and be able to select the best one and improve it further. This course is ideal for individuals who are Anyone interested in Data Science and Machine Learning. or Students who want a head start in Data Science field. or Data analysts who want to upgrade their skills in Machine Learning. or People who want to add value to their work and business by using Machine Learning. or People with basics understanding of classical machine learning algorithms like linear regression or logistic regression, but want to learn more about it. or People interested in understanding application of machine learning algorithms on real business problems. or People interested in understanding how a machine learning algorithm works and whats the math behind it. It is particularly useful for Anyone interested in Data Science and Machine Learning. or Students who want a head start in Data Science field. or Data analysts who want to upgrade their skills in Machine Learning. or People who want to add value to their work and business by using Machine Learning. or People with basics understanding of classical machine learning algorithms like linear regression or logistic regression, but want to learn more about it. or People interested in understanding application of machine learning algorithms on real business problems. or People interested in understanding how a machine learning algorithm works and whats the math behind it.

Enroll now: Machine Learning Made Easy : Beginner to Advanced using R

Summary

Title: Machine Learning Made Easy : Beginner to Advanced using R

Price: $54.99

Average Rating: 4.58

Number of Lectures: 129

Number of Quizzes: 11

Number of Published Lectures: 129

Number of Published Quizzes: 11

Number of Curriculum Items: 140

Number of Published Curriculum Objects: 140

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

R Programming, Data Handling and Cleaning, Basic Statistics, Classical Machine Learning Algorithms, Model Selection and Validation, Advanced Machine Learning Algorithms, Ensemble Learning.

Write your own R scripts and work in R environment.

Import, manipulate, clean up, sanitize and export datasets.

Understand basic statistics and implement using R.

Understand data science life cycle while understanding steps of building, validating, improving and implementing the machine learning models.

Do powerful analysis on data, find insights and present them in visual manner.

Learn classical algorithms like Linear Regression, Logistic Regression, Decision Trees and advance machine learning algorithms like SVM, Artificial Neural Networks, Reinforced Learning, Random Forests and Boosting and clustering algorithms like K-means.

Know how each machine learning algorithm works and which one to choose according to the type of problem.

Build more than one powerful machine learning model and be able to select the best one and improve it further.

Who Should Attend

Anyone interested in Data Science and Machine Learning.

Students who want a head start in Data Science field.

Data analysts who want to upgrade their skills in Machine Learning.

People who want to add value to their work and business by using Machine Learning.

People with basics understanding of classical machine learning algorithms like linear regression or logistic regression, but want to learn more about it.

People interested in understanding application of machine learning algorithms on real business problems.

People interested in understanding how a machine learning algorithm works and whats the math behind it.

Target Audiences

Anyone interested in Data Science and Machine Learning.

Students who want a head start in Data Science field.

Data analysts who want to upgrade their skills in Machine Learning.

People who want to add value to their work and business by using Machine Learning.

People with basics understanding of classical machine learning algorithms like linear regression or logistic regression, but want to learn more about it.

People interested in understanding application of machine learning algorithms on real business problems.

People interested in understanding how a machine learning algorithm works and whats the math behind it.

Want to know how Machine Learning algorithms work and how people apply it to solve data science problems??You are looking at right course!

This course has been created, designed and assembled by professional Data Scientists who have worked in this field for nearly a decade. We can help you understand the complex machine learning algorithms while keeping you grounded to the implementation on real business and data science problems.

We will let you feel the water and coach you to become a full swimmer in the realm of data science and Machine Learning. Every tutorial will increase your skill level by challenging your ability to foresee, yet letting you improve upon self.

We are sure that you will have fun while learning from our tried and tested structure of course to keep you interested in what’s coming next.

Here is how the course is going to work:

Part 1 – Introduction to R Programming.

This is the part where you will learn basic of R programming and familiarize yourself with R environment.

Be able to import, export, explore, clean and prepare the data for advance modeling.

Understand the underlying statistics of data and how to report/document the insights.

Part 2 – Machine Learning using R

Learn, upgrade and become expert on classic machine learning algorithms like Linear Regression, Logistic Regression and Decision Trees.

Learn which algorithm to choose for specific problem, build multiple model, learn how to choose the best model and be able to improve upon it.

Move on to advance machine learning algorithms like SVM, Artificial Neural Networks, Reinforced Learning, Random Forests and Boosting and clustering algorithms like K-means.

Features:

Fully packed with LAB Sessions. One to learn from and one for you to do it yourself.

Course includes R?code, Datasets and other supporting material at the beginning of each section for you to download and use on your own.

Quiz after each section to test your learning.

Bonus:

This course is packed with 5 projects on real data related to different domains to prepare you for wide variety of business problems.

These projects will serve as your step by step guide to solve different business and data science problems.

Course Curriculum

Chapter 1: Introduction to R

Lecture 1: Getting Started

Lecture 2: R Environment

Lecture 3: R Packages

Lecture 4: R Data types Vectors

Lecture 5: R Data Frames

Lecture 6: List

Lecture 7: Factor and Matrix

Lecture 8: R History and Scripts

Lecture 9: R Functions

Lecture 10: Errors

Chapter 2: Data Handling in R

Lecture 1: Introduction to Data Handling

Lecture 2: Importing the Datasets

Lecture 3: Checklist

Lecture 4: Subsetting the Data

Lecture 5: Subsetting Variable Condition

Lecture 6: Calculated Fields_ifelse

Lecture 7: Sorting and Duplicates

Lecture 8: Joining and Merging

Lecture 9: Exporting the Data

Chapter 3: Basic Statistics and Graph

Lecture 1: Introduction and Sampling

Lecture 2: Descriptive Statistics

Lecture 3: Percentiles and Quartiles

Lecture 4: Box Plots

Lecture 5: Creating Graphs and Conclusions

Chapter 4: Data Cleaning and Treatment

Lecture 1: Introduction to Data Cleaning and Model Building Cycle

Lecture 2: Model Building Cycle

Lecture 3: Data Cleaning Case Study

Lecture 4: CS lab step one basic content of dataset

Lecture 5: Variable Level Exploration Catagorical

Lecture 6: Reading Data Dictionary

Lecture 7: Step two Lab Categorical Variable Exploration

Lecture 8: Step three Lab Variable Level Exploration Continues

Lecture 9: Data Cleaning and Treatment

Lecture 10: Step four Treatment-Scenario 1

Lecture 11: Step four Treatment-Scenario 2

Lecture 12: Data Cleaning Scenario 3

Lecture 13: Some Other Variables

Lecture 14: Conclusions

Chapter 5: Linear Regression

Lecture 1: Introduction and Correlation

Lecture 2: LBA Correlation Calculation in R

Lecture 3: Beyond Pearson Correlation

Lecture 4: From Correlation to Regression

Lecture 5: Regression Line Fitting in R

Lecture 6: R Squared

Lecture 7: Multiple Regression

Lecture 8: Adjusted R Squared

Lecture 9: Issue with Multiple Regression

Lecture 10: Multicollinearity

Lecture 11: Regression Conclusion

Chapter 6: Logistic Regression

Lecture 1: Need of Non-Linear Regression

Lecture 2: Logistic Function and Line

Lecture 3: Multiple Logistic Regression

Lecture 4: Goodness of Fit for a Logistic Regression

Lecture 5: Multicollinearity in Logistic Regression

Lecture 6: Individual Impact of Variables

Lecture 7: Model Selection

Lecture 8: Logistic Regression Conclusion

Chapter 7: Decision Tree

Lecture 1: Introduction to Decision Tree and Segmentation

Lecture 2: The Decision Tree Philosophy & The Decision Tree Approach

Lecture 3: The Splitting Criterion & Entropy Calculation

Lecture 4: Information Gain & Calculation

Lecture 5: The Decision Tree Algorithm

Lecture 6: Split for Variable & The Decision Tree Lab – Part 1

Lecture 7: The Decision Tree Lab – Part 2 & Validation

Lecture 8: The Decision Tree Lab – Part 3 & Overfitting

Lecture 9: Pruning & Complexity Parameters

Lecture 10: Choosing Cp & Cross Validation Error

Lecture 11: Two Types of Pruning

Lecture 12: Tree Building and Model Selection

Lecture 13: Conclusion

Chapter 8: Model Selection and Cross Validation

Lecture 1: Introduction to Model Selection

Lecture 2: Sensitivity Specificity

Lecture 3: Sensitivity Specificity Continued

Lecture 4: ROC AUC

Lecture 5: The Best Model

Lecture 6: Errors

Lecture 7: Overfitting Underfitting

Lecture 8: Bias_Variance Treadoff

Lecture 9: Holdout Data Validation

Lecture 10: Ten fold CV

Lecture 11: Kfold CV

Lecture 12: MSCV Conclusion

Chapter 9: Neural Networks

Lecture 1: Introduction and Logistic Regression Recap

Lecture 2: Decision Boundary

Instructors

Venkata Reddy AI Classes
Data Science starts here!

Rating Distribution

1 stars: 3 votes

2 stars: 3 votes

3 stars: 12 votes

4 stars: 35 votes

5 stars: 50 votes

Frequently Asked Questions

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