Certificate Course in Machine
Learning Using R Programming
 
Become a Machine Learning specialist and gain
a head start in your machine learning career.
funding-schemes sbl-khas-black-centered
5 Days / 40 Hours / 26 Modules
+ 3 month coaching + 1 year eLearning access
Get in Touch With Us
Certificate Course
in Machine
Learning Using R
Programming
Become a Machine
Learning specialist,
gain holistic knowledge
in ML algorithms.
5 Days / 40 Hours / 26 Modules
+ 3 month coaching + 1 year eLearning access
funding-schemes-sbl-khas-white-centered
Master the fundamentals of Machine Learning using R programming and deep-dive into the world of analytics and ML.
In this course, we focus on the field of ML and describe the well-known processes, algorithms, and tools for one to be a successful ML practitioner. Students will be able to build skills in data acquisition and modelling, classification, and regression. In addition, students will also get to explore highly essential tasks such as model validation, optimization, scalability, and real-time streaming.

Duration

5 days / 40 hours

Level

Beginner to Intermediate

Delivery

100% Online - Instructor Led
  • Course Code

    ML102
  • Audience

    Anyone with interest in Machine Learning And R programming
  • Language

    English
  • Certification

    Participants will receive a certificate of competence.
 
Tools Covered For This Course:
 
logo
dplyr-logo
 
 
Key Learning Outcomes

This course will also give students a chance to understand the fundamental issues and challenges of machine learning which include data, model selection, and model complexity. This course will equip you with the necessary skills needed to excel in this field. By the end of the training program, you will be able to:
  • Use R for Machine Learning Algorithms
  • Become familiar with Machine Learning algorithms including Black Box techniques such as Neural Networks and Support Vector Machine utilizing R.
  • Become familiar with Regression algorithms and the application of R as statistical software in Machine Learning
  • Create Data Visualizations
  • Have sufficient understanding of R programming.
 
 
Key Learning Outcomes

This course will also give students a chance to understand the fundamental issues and challenges of machine learning which include data, model selection, and model complexity. This course will equip you with the necessary skills needed to excel in this field. By the end of the training program, you will be able to:
  • Become familiar with analyzing data, computing statistical measures along with Data Wrangling, Data Cleansing, Data Manipulation, etc.
  • Become familiar with Machine Learning algorithms including Black Box techniques such as Neural Networks and Support Vector Machine.
  • Become familiar with Regression algorithms and the application of Python, R as statistical software in Machine Learning and Data Science.
  • Build predictive models.
  • Be able to create Data Visualization, Data Manipulation in different forms and draw meaningful business insights from the underlying data.
 
Course Modules Covered in the Machine Learning Using R programming course
Day 1 - Overview, Introduction, Variable types and data structures, Programming Structures

Overview

  1. History of R
  2. Advantages and disadvantages
  3. Downloading and installing
  4. How to find documentation

Introduction

  1. Using the R console
  2. Getting help
  3. Learning about the environment
  4. Writing and executing scripts
  5. Object oriented programming
  6. Introduction to vectorized calculations
  7. Introduction to data frames
  8. Installing packages
  9. Working directory & Saving your work

Variable types and data structures

  1. Variables and assignment
  2. Data types
  3. Data structures
  4. Indexing, sub setting
  5. Assigning new values
  6. Viewing data and summaries
  7. Naming conventions
  8. Objects

Programming Structures

  1. For Loops
  2. While Loops
  3. Repeat Loops
  4. Nested For Loops
  5. Conditional Statements
  6. Nested Conditional Statements
  7. Loops and Conditional Statements
  8. User Defined Functions
  9. The Return Command
  10. More Complex Functions Examples
  11. Checking Whether an Integer Is a Perfect Square
  12. A Custom Function That Solves Quadratic Equations
  13. Binary Operations
Day 2 - Getting data into the R environment, Vectors, Factors, Lists, Dataframe manipulation with dplyr

Getting data into the R environment

  1. Built-in data
  2. Reading data from structured text files
  3. Reading data using ODBC

Vectors

  1. Create a vector
  2. Naming a vector
  3. Calculating vector
  4. Comparing vector

Factors

  1. What is a Factor
  2. Factor Levels
  3. Ordered Factors
  4. Comparing ordered Factors

Lists

  1. What is a List
  2. Creating a List
  3. Creating a named List
  4. Selecting elements from a List

Dataframe manipulation with dplyr

  1. Renaming columns
  2. Adding new columns
  3. Binning data (continuous to categorical)
  4. Combining categorical values
  5. Transforming variables
  6. Handling missing data
  7. Long to wide and back
  8. Merging datasets together
  9. Stacking datasets together (concatenation)
Day 3 - Handling dates in R, Control flow, Functions in depth , Applying functions across dimensions, Exploratory data analysis (descriptive statistics), Matrices

Handling dates in R

  1. Date and date-time classes in R
  2. Formatting dates for modelling

Control flow

  1. Truth testing
  2. Branching
  3. Looping

Functions in depth

  1. Parameters
  2. Return values
  3. Variable scope
  4. Exception handling

Applying functions across dimensions

  1. apply
  2. sapply
  3. lapply

Exploratory data analysis (descriptive statistics)

  1. Continuous data
  2. Categorical data
  3. Group by calculations with dplyr
  4. Melting and casting data

Matrices

  1. Introduction to Matrices
  2. Naming Dimensions
  3. Colnames() and Rownames()
  4. Matrix Operators
  5. Visualizing with Mapplot()
  6. Subsetting
  7. Visualizing Subsets
Day 4 - Inferential statistics, High-level plotting commands, Base graphics, Graphics parameters list, Advanced R graphics: ggplot2, General linear regression, Machine Learning with R

Inferential statistics

  1. Bivariate correlation
  2. T-test and non-parametric equivalents
  3. Chi-squared test

High-level plotting commands

  1. The plot() function
  2. Displaying multivariate data
  3. Display graphics
  4. Arguments to high-level plotting fun

Base graphics

  1. Base graphics system in R
  2. Scatterplots, histograms, barcharts, box and whiskers, dotplots
  3. Labels, legends, titles, axes
  4. Exporting graphics to different formats

Graphics parameters list

  1. Graphical elements
  2. Axes and tick marks
  3. Figure margins
  4. Multiple figure environment
  5. The par() function
  6. Arguments to graphics functions

Advanced R graphics: ggplot2

  1. Understanding the grammar of graphics
  2. Quick plots (qplot function)
  3. Building graphics by pieces (ggplot function)

General linear regression

  1. Linear and logistic models
  2. Regression plots
  3. Confounding / interaction in regression
  4. Scoring new data from models (prediction)

Machine Learning with R

  1. Linear Regression
  2. Logistic Regression
  3. K-Nearest Neighbours
  4. Decision Trees
  5. Random Forests
  6. Support Vector Machines
  7. K-means Clustering
Day 5 - What is Shiny?, Widgets and the Input List Elements They Create, Application Layout, Shiny Extensions

What is Shiny?

  1. A Simple App
  2. The Shiny App Directory
  3. ui.R
  4. shiny.R
  5. global.R
  6. R and shinyServer
  7. The www Directory
  8. runApp

High-level plotting commands

  1. verbatimTextOutput
  2. Input List Elements and Their Role in shinyServer
  3. Output List Elements and Their Role in shinyUI
  4. Application Development Exercises

Base graphics

  1. sidebar
  2. The Bootstrap 12-Wide Grid System
  3. tabsets, navlist, and navbarPage
  4. Application Themes
  5. Showcase Mode and the DESCRIPTION File
  6. The Reactive Dependency Chain

Graphics parameters list

  1. DataTables
  2. dygraphs
  3. shinyRGL
  4. Launching Via shinyapps.io
  5. Conclusion

Key Features

  • 40 hours of instructor led training
  • Fully Online
  • Class recording available
  • Interactive Learning
  • 3 month Coaching Session
  • 100% HRDF SBL-KHAS Claimable!

Pre-Requisites

  • Basic Programming Knowledge
  • Analytical Mindset
  • Willingness to self learn online
  • No prior experience is required
  • We will start from the very basics
  • Committed to complete all tasks

Who Should Join

  • Professional switching careers
  • Business Analysts
  • IT Engineers
  • Students
  • New Programmers
  • Anyone interested in R programming
Program Key Highlights
online-learning-2
40 hours of Remote Online Learning
learning-hours
80 Additional Self Learning Hours
hands-on
12 Live Hands-on Projects
certification
Certified by International Body
mentor
Mentorship with Industry Experts
industry
Designed for Beginners & Professionals
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