Machine Learning for Business Professionals

Overview This Machine Learning for Business Professionals course provides an in-depth exploration of how machine learning and data science can revolutionise business …

Machine Learning for Business Professionals

Machine Learning for Business Professionals

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Machine Learning for Business Professionals Overview

This Machine Learning for Business Professionals course provides an in-depth exploration of how machine learning and data science can revolutionise business decision-making. Starting with the basics of R programming, you’ll learn how to manage and visualise data effectively, progressing to more complex machine learning techniques. By the end of this course, you’ll be equipped to apply predictive models and generate insights that can significantly impact business strategies.

Learning Outcomes

  • Understand the fundamentals of R programming and data science.
  • Manipulate and analyse data using R.
  • Create powerful data visualisations to interpret and present data.
  • Develop proficiency in intermediate R programming concepts.
  • Utilise R Markdown for dynamic report generation.
  • Build interactive web applications using R Shiny.
  • Gain a foundational understanding of machine learning concepts.
  • Apply machine learning techniques to solve real business problems.
  • Create end-to-end data analysis workflows.
  • Prepare for a successful career in data science within business contexts.

Who Is This Course For

This course is ideal for business professionals who aim to use data science and machine learning to enhance their organisations’ decision-making processes. Whether you’re a business analyst, marketing professional, or manager looking to integrate data-driven strategies into your work, this course will equip you with the necessary skills.

Eligibility Requirements

To enrol in this course, you should have a basic understanding of statistics and some exposure to programming. It’s designed for professionals seeking to enhance their analytical skills and apply machine learning in business contexts, making it suitable for beginners and intermediate learners alike.

Entry Requirements

  • Age Requirement: Applicants must be 16 or older, making the course accessible to both young learners and adults.
  • Academic Background: No prior qualifications required, open to all backgrounds.
  • Language Proficiency: A good understanding of English is essential, as all lessons are in English.
  • Numeracy Skills: Basic writing and numeracy skills are needed to follow the course content.

Why Choose Us

  • Affordable, engaging & high-quality e-learning study materials;
  • Tutorial videos/materials from the industry-leading experts;
  • Study in a user-friendly, advanced online learning platform;
  • Efficient exam systems for the assessment and instant result;
  • The UK & internationally recognised accredited
  • Access to course content on mobile, tablet or desktop from anywhere, anytime;
  • The benefit of career advancement opportunities;
  • 24/7 student support via email.

Career Path

This course will prepare you to become a key player in business analytics. With the skills you acquire, you can pursue roles such as Data Analyst, Business Intelligence Analyst, or Machine Learning Engineer. These positions allow you to directly influence strategic decisions, improve operational efficiency, and drive business growth.

Course Curriculum

Data Science and Machine Learning Course Intro
Data Science and Machine Learning Introduction 00:03:00
What is Data Science 00:10:00
Machine Learning Overview 00:05:00
Who is This Course for 00:03:00
Data Science and Machine Learning Marketplace 00:05:00
Data Science and Machine Learning Job Opportunities 00:03:00
Getting Started with R
Getting Started 00:11:00
Basics 00:06:00
Files 00:11:00
RStudio 00:07:00
Tidyverse 00:05:00
Resources 00:04:00
Data Types and Structures in R
Unit Introduction 00:30:00
Basic Type 00:09:00
Vector Part One 00:20:00
Vectors Part Two 00:25:00
Vectors – Missing Values 00:16:00
Vectors – Coercion 00:14:00
Vectors – Naming 00:10:00
Vectors – Misc 00:06:00
Creating Matrics 00:31:00
List 00:32:00
Introduction to Data Frames 00:19:00
Creating Data Frames 00:20:00
Data Frames: Helper Functions 00:31:00
Data Frames Tibbles 00:39:00
Intermediate R
Intermediate Introduction 00:47:00
Relational Operations 00:11:00
Conditional Statements 00:11:00
Loops 00:08:00
Functions 00:14:00
Packages 00:11:00
Factors 00:28:00
Dates and Times 00:30:00
Functional Programming 00:37:00
Data Import or Export 00:22:00
Database 00:27:00
Data Manipulation in R
Data Manipulation in R Introduction 00:36:00
Tidy Data 00:11:00
The Pipe Operator 00:15:00
The Filter Verb 00:22:00
The Select Verb 00:46:00
The Mutate Verb 00:32:00
The Arrange Verb 00:10:00
The Summarize Verb 00:23:00
Data Pivoting 00:43:00
JSON Parsing 00:11:00
String Manipulation 00:33:00
Web Scraping 00:59:00
Data Visualization in R
Data Visualization in R Section Intro 00:17:00
Getting Started 00:16:00
Aesthetics Mappings 00:25:00
Single Variable Plots 00:37:00
Two Variable Plots 00:21:00
Facets, Layering, and Coordinate Systems 00:18:00
Styling and Saving 00:12:00
Creating Reports with R Markdown
Creating with R Markdown 00:29:00
Building Webapps with R Shiny
Introduction to R Shiny 00:26:00
A Basic R Shiny App 00:31:00
Other Examples with R Shiny 00:34:00
Introduction to Machine Learning
Machine Learning Part 1 00:22:00
Machine Learning Part 2 00:47:00
Starting A Career in Data Science
Starting a Data Science Career Section Overview 00:03:00
Data Science Resume 00:04:00
Getting Started with Freelancing 00:05:00
Top Freelance Websites 00:05:00
Personal Branding 00:05:00
Importance of Website and Blo 00:04:00
Networking Do’s and Don’ts 00:04:00
Resources
Resources – Data Science & Machine Learning with R 00:00:00
Assignment
Assignment – Machine Learning for Business Professionals 00:00:00
Order Certificate
Order Certificate 00:00:00

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