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Machine Learning using R
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Machine Learning using R Training in London, United Kingdom

Learn about data classification using algorithms and clustering in R. Including the concepts of regression, forecasting, and visualizing the time series data.

Online & Classroom | Latest & Accredited Courseware | 100+ hrs of assignments.

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Modes of Delivery

Key Features

Learn the implementation of general predictive modelling in R language
Lab exercises for complete hands on expertise in R
Get coached by certified experts
Learn to build your own functions, objects and packages using R
Flexible anytime, anyplace learning
40 hours of hands-on training


If you want to make a career in predictive analysis or work with big data then you have to understand machine learning and R is the most popular platform for applied machine learning. This open source programming language is among the most widely used languages for statistics and data mining, gives quick results and is supported by a worldwide community of users and developers.

Zeolearn academy’s comprehensive Machine Learning using R course is a must for those who see themselves as future analysts. The aim of this course is to familiarise you with statistical models for supervised and unsupervised learning using R programming language and how the R language environment supports predictive modelling with machine learning. Enrol now for a complete hands on familiarity on Machine Learning in R.

Here’s what you will learn!

  • How general predictive modeling and unsupervised learning are implemented in R
  • How data is classified using classification algorithms and clustering in R
  • The concept of regression, forecasting and visualizing the time series data
  • Important models such as tree based, support vector machines and other fundamentals


  • Basic knowledge of a programming language such as Python or Java
  • A background in Mathematics will be beneficial



  • What is machine learning
  • Explanation and prediction
  • Learning system model
  • Terminology
  • Training and testing
  • Performance
  • Algorithms
  • Structure
  • The Standard Linear Model
  • Logistic Regression
  • Expansions of Tools
    • Generalised Linear Models
    • Generalised Additive Models 
  • Correlation
  • Strength of linear association
  • Least-squares or regression line
  • Linear regression model
  • Correlation coefficient R
  • Multiple regression
  • Regression diagnostics
  • Assumptions in Regression Analysis
  • Continuous Outcomes
    • Squared Error
    • Absolute Error 
    • Negative Log-likelihood
    • R Example
  • Categorical Outcomes
    • Misclassification
    • Binomial log-likelihood
    • Exponential
    • Hinge Loss
  • Bias & Variance
  • The Tradeoff
  • Diagnosing Bias-Variance Issues & Possible Solutions
    • Worst Case Scenario
    • High Variance
    • High Bias 
  • Adding Another Validation Set
  • K-fold Cross-Validation
    • Leave-one-out Cross-Validation
  • Bootstrap 
  • Beyond Classification Accuracy: Other Measures of Performance 
  • Data Preparation
    • Define Data and Data Partitions
    • Feature Scaling
    • Feature Engineering
    • Discretization
  • Model Selection
  • Model Assessment 
  • The Data set
  • R Implementation
  • Feature Selection & The Data Partition
  • k-nearest Neighbours
    • Strengths & Weaknesses
  • Neural Nets
    • Strengths & Weaknesses
  • Trees & Forests
    • Strengths & Weaknesses
  • Support Vector Machines
    • Strengths & Weaknesses 
  • Unsupervised Learning
    • Clustering
    • Latent Variable Models
    • Graphical Structure
    • Imputation
  • Ensembles
    • Bagging
    • Boosting
    • Stacking
  • Feature Selection & Importance
  • Textual Analysis
  • Bayesian Approaches
  • Naive Bayes learning
  • Conditional probability
  • Bayesian theorem: basics
  • The Bayes classifier
  • Model parameters
  • Naive Bayes training
  • Types of errors
  • Sensitivity and specificity
  • ROC curve
  • Holdout estimation
  • Cross-validation
  • Key requirements
  • Decision tree as a rule set
  • How to create a decision tree
  • Choosing attributes
  • ID3 heuristic
  • Entropy
  • Pruning trees - Pre and post
  • Subtree Replacement
  • Raising
  • Tree induction
  • Splitting based on ordinal attributes
  • How to determine the best split
  • Measure of impurity: GINI
  • Splitting based on GINI
  • Attributes binary
  • Categorical -GINI
  • Strengths and weakness of decision trees
  • Cautionary Notes
  • Some Guidelines
  • Conclusion 

Frequently Asked Questions

Machine learning is often used to make informed preventive or proactive decisions with its powerful predictive capabilities. With the help of algorithms, machine learning helps find insights and patterns in data. This automatic application of complex mathematical calculations to big data is revolutionising the world. R is a programming language that is the most popular platform for applied machine learning and is widely used to statistically explore data sets. Studying R in machine learning will help you become a proficient data analyst.

After completing our course, you will know:

  • About R, its basic data structures, objects and operations
  • Basic plots and lattice plots
  • Basic statistics and machine learning fundamentals
  • Classification of data using Naïve-Bayes classifier
  • Support vector machine and its implementation in R
  • Common classifier algorithms such as Decision Tree

Towards the end of the course, all participants will be required to work on a project to get hands on familiarity with the concepts learnt. You will use R programming language and perform statistical data analysis with support from your mentors. This project, which can also be a live industry project, will be reviewed by our instructors and industry experts. On successful completion, you will be awarded a certificate.

Classes are held on weekdays and weekends. You can check available schedules and choose the batch timings which are convenient for you.

You may be required to put in 10 to 12 hours of effort every week, including the live class, self study and assignments.

We offer classes in classroom and online format. While classroom sessions are held in specific venues in your city, for online sessions all you need is a Windows computer with good internet connection and you can access the class anywhere, at anytime. A headset with microphone is also recommended.

You may also attend these classes from your smart phone or tablet.

Don’t worry, you can always access your class recording or opt to attend the missed session again in any other live batch.

Statisticians, Data Analysts, Business Analysts and professionals keen to learn more about the science and practice of Machine Learning using R, will benefit from this course.

One must have:

  • Operating system such as Mac OS X, Windows or Linux
  • Good Text / JavaScript Editor (Notepad++ / SublimeText / Brackets / Atom )
  • A modern web browser such as Chrome
  • High speed Internet Connection

Machine Learning using R Course in London

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