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 CodeML102
- AudienceAnyone with interest in Machine Learning And R programming
- LanguageEnglish
- CertificationParticipants will receive a certificate of competence.

Tools Covered For This Course:

Course Modules Covered in the Machine Learning Using R programming course

Day 1 - Overview, Introduction, Variable types and data structures, Programming Structures

#### Overview

- History of R
- Advantages and disadvantages
- Downloading and installing
- How to find documentation

#### Introduction

- Using the R console
- Getting help
- Learning about the environment
- Writing and executing scripts
- Object oriented programming
- Introduction to vectorized calculations
- Introduction to data frames
- Installing packages
- Working directory & Saving your work

#### Variable types and data structures

- Variables and assignment
- Data types
- Data structures
- Indexing, sub setting
- Assigning new values
- Viewing data and summaries
- Naming conventions
- Objects

#### Programming Structures

- For Loops
- While Loops
- Repeat Loops
- Nested For Loops
- Conditional Statements
- Nested Conditional Statements
- Loops and Conditional Statements
- User Defined Functions
- The Return Command
- More Complex Functions Examples
- Checking Whether an Integer Is a Perfect Square
- A Custom Function That Solves Quadratic Equations
- Binary Operations

Day 2 - Getting data into the R environment, Vectors, Factors, Lists, Dataframe manipulation with dplyr

#### Getting data into the R environment

- Built-in data
- Reading data from structured text files
- Reading data using ODBC

#### Vectors

- Create a vector
- Naming a vector
- Calculating vector
- Comparing vector

#### Factors

- What is a Factor
- Factor Levels
- Ordered Factors
- Comparing ordered Factors

#### Lists

- What is a List
- Creating a List
- Creating a named List
- Selecting elements from a List

#### Dataframe manipulation with dplyr

- Renaming columns
- Adding new columns
- Binning data (continuous to categorical)
- Combining categorical values
- Transforming variables
- Handling missing data
- Long to wide and back
- Merging datasets together
- 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

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

#### Control flow

- Truth testing
- Branching
- Looping

#### Functions in depth

- Parameters
- Return values
- Variable scope
- Exception handling

#### Applying functions across dimensions

- apply
- sapply
- lapply

#### Exploratory data analysis (descriptive statistics)

- Continuous data
- Categorical data
- Group by calculations with dplyr
- Melting and casting data

#### Matrices

- Introduction to Matrices
- Naming Dimensions
- Colnames() and Rownames()
- Matrix Operators
- Visualizing with Mapplot()
- Subsetting
- 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

- Bivariate correlation
- T-test and non-parametric equivalents
- Chi-squared test

#### High-level plotting commands

- The plot() function
- Displaying multivariate data
- Display graphics
- Arguments to high-level plotting fun

#### Base graphics

- Base graphics system in R
- Scatterplots, histograms, barcharts, box and whiskers, dotplots
- Labels, legends, titles, axes
- Exporting graphics to different formats

#### Graphics parameters list

- Graphical elements
- Axes and tick marks
- Figure margins
- Multiple figure environment
- The par() function
- Arguments to graphics functions

#### Advanced R graphics: ggplot2

- Understanding the grammar of graphics
- Quick plots (qplot function)
- Building graphics by pieces (ggplot function)

#### General linear regression

- Linear and logistic models
- Regression plots
- Confounding / interaction in regression
- Scoring new data from models (prediction)

#### Machine Learning with R

- Linear Regression
- Logistic Regression
- K-Nearest Neighbours
- Decision Trees
- Random Forests
- Support Vector Machines
- K-means Clustering

Day 5 - What is Shiny?, Widgets and the Input List Elements They Create, Application Layout, Shiny Extensions

#### What is Shiny?

- A Simple App
- The Shiny App Directory
- ui.R
- shiny.R
- global.R
- R and shinyServer
- The www Directory
- runApp

#### High-level plotting commands

- verbatimTextOutput
- Input List Elements and Their Role in shinyServer
- Output List Elements and Their Role in shinyUI
- Application Development Exercises

#### Base graphics

- sidebar
- The Bootstrap 12-Wide Grid System
- tabsets, navlist, and navbarPage
- Application Themes
- Showcase Mode and the DESCRIPTION File
- The Reactive Dependency Chain

#### Graphics parameters list

- DataTables
- dygraphs
- shinyRGL
- Launching Via shinyapps.io
- 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

40 hours of Remote Online Learning

80 Additional Self Learning Hours

12 Live Hands-on Projects

Certified by International Body

Mentorship with Industry Experts

Designed for Beginners & Professionals

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*Payments are only accepted once applicants are enrolled into the program.*

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