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 what's 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 what's 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 what's 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 what's 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
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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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