Mastering R Programming and Machine Learning
Specialization | 36 Course Series | 25 Mock Tests
This R Programming Course includes 36 courses with 150 hours of video tutorials and One year access and several mock tests for practice. You will also get verifiable certificates (unique certification number and your unique URL) when you complete each of them. It will explain you R Programming, Machine learning using R, Business Analytics using R, Data Visualization using R, Customer Analytics using R, Marketing Analytics using R among others.
Enroll now and get a FREE Exam Voucher worth $285!
Offer ends in:
What you'll get
- 150 Hours
- 36 Courses
- Course Completion Certificates
- One year access
- Self-paced Courses
- Technical Support
- Mobile App Access
- Case Studies
Synopsis
- Courses: You get access to all 36 courses, Projects bundle. You do not need to purchase each course separately.
- Hours: 150 Video Hours
- Core Coverage: R Programming, Machine learning using R, Business Analytics using R, Data Visualizing using R, Customer Analytics using R, Marketing Analytics using R
- Course Validity: One year access
- Eligibility: Anyone serious about learning R Programming and wants to make a career in this Field
- Pre-Requisites: Familiarity with R programming language is recommended
- What do you get? Certificate of Completion for each of the 36 courses, Projects
- Certification Type: Course Completion Certificates
- Verifiable Certificates? Yes, you get verifiable certificates for each course with a unique link. These link can be included in your resume/LinkedIn profile to showcase your enhanced R Programming skills
- Type of Training: Video Course – Self Paced Learning
Content
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Section 1: Introduction to R Programming and Machine Learning Basics
Courses No. of Hours Certificates Details R Studio UI and R Script Basics 4h 13m ✔ Decision Trees Modeling using R 1h 4m ✔ Decision Trees - Bank Loan Default Prediction using R 1h 47m ✔ Logistic Regression & Supervised Machine Learning with R 4h 14m ✔ Project on ML - Churn Prediction Model using R Studio 1h 22m ✔ R Programming for Data Science | A Complete Courses to Learn 5h 7m ✔ Test - R Programming Basic Test Test - Test Series R Programming Test - 2023 R Programming Exam Test - R Programming Complete Exam Test - R Programming Practice Test -
Section 2: Advanced Supervised Machine Learning with R
Courses No. of Hours Certificates Details Supervised Machine Learning with R 2024 - Linear Regression 3h 05m ✔ Machine Learning with R 20h 25m ✔ Time Series Analysis and Forecasting using R 4h 34m ✔ Project - Fraud Analytics using R 2h 34m ✔ Project - Marketing Analytics using R and Microsoft Excel 2h 9m ✔ Case Study - Customer Analytics using Tableau and R 2h 7m ✔ Case Study - Pricing Analytics using Tableau and R 2h 39m ✔ Cluster Analysis and Unsupervised Machine Learning - K-Means Clustering using R 43m ✔ Machine Learning Project using Caret in R 1h 58m ✔ Test - Machine Learning Assessment Test - ML Assessment Exam Test - Mock Exam Machine Learning Test - Complete Machine Learning Exam Test - Machine Learning Ultimate Exam -
Section 3: Specialized Topics in Data Science and Analytics with R
Courses No. of Hours Certificates Details Business Analytics using R - Hands-on! 16h 21m ✔ Data Science with R 6h 2m ✔ Comprehensive Course on R 3h 54m ✔ Project - Market Basket Analysis in R 37m ✔ Project - Hypothesis Testing using R 3h 6m ✔ Data Visualization with R Shiny - The Fundamentals 39m ✔ R Studio Anova Techniques Course 2h 18m ✔ Test - R Programming Basic Test Test - Test Series R Programming Test - 2023 R Programming Exam Test - R Programming Complete Exam Test - R Programming Practice Test -
Section 4: Capstone Projects and Practical Applications
Courses No. of Hours Certificates Details Project - Exploratory Data Analysis EDA using ggplot2, R and Linear Regression 2h 07m ✔ Project on R - HR Attrition and Analytics 2h 08m ✔ Predictive Analytics Model for Term Deposit Investment with R Studio 3h 2m ✔ Project on R - Card Purchase Prediction 2h 28m ✔ Random Forest Techniques and R - Employee Attrition Prediction 1h 6m ✔ Predictive Analytics Model for Term Deposit Investment using CART Algorithm 1h 38m ✔ Machine Learning Project using Caret in R 1h 58m ✔ Cluster Analysis and Unsupervised Machine Learning - K-Means Clustering using R 43m ✔ R Practical - Telecom Customer Churn Prediction 1h 27m ✔ -
Section 5: Advanced Financial Analytics with R
Courses No. of Hours Certificates Details Financial Analytics in R - Beginners 3h 45m ✔ Quantitative Analysis Using R 2h 25m ✔ R for Finance - Beginners to Beyond 2h 17m ✔ Financial Analytics in R - Intermediate 1h 28m ✔ Financial Analytics in R - Advanced 1h 35m ✔ -
Section 6: Mock Tests and Quizzes
Courses No. of Hours Certificates Details Test - Machine Learning Ultimate Exam Test - R Programming Practice Test Test - Complete Machine Learning Exam Test - R Programming Complete Exam Test - Mock Exam Machine Learning Test - 2023 R Programming Exam Test - ML Assessment Exam Test - Test Series R Programming Test - Machine Learning Assessment Test - R Programming Basic Test
Description
Course Introduction: R Programming and Machine Learning Mastery
Welcome to our comprehensive course on R programming and machine learning! In this course, we'll take you on a journey through the fundamentals of R programming and delve into advanced machine learning techniques using R. Whether you're new to programming or seeking to enhance your skills in data analysis and predictive modeling, this course has something for everyone.
Section 1: Introduction to R Programming and Machine Learning Basics
In Section 1, we'll start by laying the groundwork for your journey into R programming and machine learning. You'll learn the essentials of R Studio UI and R script basics, providing you with a solid foundation in R programming. From there, we'll explore decision tree modeling and logistic regression, two fundamental techniques in supervised machine learning. Through practical projects like churn prediction modeling, you'll gain hands-on experience applying machine learning algorithms to real-world datasets.
Section 2: Advanced Supervised Machine Learning with R
Section 2 is where we dive deeper into advanced supervised machine learning techniques. You'll explore a variety of algorithms and methodologies, including linear regression, decision trees, and support vector machines, all within the context of R programming. With a focus on practical applications, you'll work on projects such as fraud analytics and marketing analytics, honing your skills in data analysis and predictive modeling.
Section 3: Specialized Topics in Data Science and Analytics with R
In Section 3, we'll explore specialized topics in data science and analytics using R. From time series analysis and forecasting to marketing analytics and customer segmentation, you'll gain a comprehensive understanding of how to extract insights from data and make informed business decisions. Through hands-on projects and case studies, you'll apply your knowledge to solve real-world problems in various domains.
Section 4: Capstone Projects and Practical Applications
Finally, in Section 4, you'll put your skills to the test with capstone projects and practical applications. You'll work on projects ranging from fraud detection to market basket analysis, showcasing your ability to leverage R programming and machine learning techniques to solve complex problems. These projects will not only demonstrate your proficiency to potential employers but also provide valuable experience in tackling real-world data science challenges.
Get ready to embark on an exciting journey into the world of R programming and machine learning. By the end of this course, you'll have the skills and confidence to analyze data, build predictive models, and extract actionable insights using R. Let's dive in.
Section 5: Advanced Financial Analytics with R
In Section 5, we'll delve into advanced financial analytics using R, catering to learners interested in the intersection of finance and data science. From quantitative analysis to financial modeling, you'll explore various techniques for analyzing financial data and making informed investment decisions. With courses covering topics like financial analytics for beginners and advanced users, you'll gain a comprehensive understanding of how to leverage R for financial analysis and portfolio management.
Section 6: Mock Tests and Quizzes
Finally, in Section 6, we'll provide you with mock tests and quizzes to assess your understanding and reinforce your learning. These assessments will cover the material taught in the previous sections, allowing you to gauge your proficiency and identify areas for improvement. By completing these tests, you'll solidify your knowledge and readiness to apply R programming and machine learning techniques in real-world scenarios.
Conclusion: Mastering R Programming and Machine Learning
Throughout this course, our goal is to empower you with the skills and knowledge needed to excel in R programming and machine learning. Whether you're a beginner looking to build a foundation in data science or an experienced professional seeking to expand your skill set, this course offers a comprehensive learning experience tailored to your needs. Get ready to unlock the full potential of R programming and machine learning and embark on a rewarding journey into the world of data science and analytics. Let's get started!
Sample Certificate
Requirements
- Basic Programming Skills: A fundamental understanding of programming concepts is recommended, including variables, loops, conditionals, and functions. While prior experience with R programming is not required, familiarity with any programming language will be beneficial.
- Mathematics Fundamentals: Basic knowledge of mathematics, including algebra, calculus, and statistics, is essential. Understanding concepts such as linear regression, probability distributions, and hypothesis testing will facilitate comprehension of machine learning algorithms.
- Data Analysis Proficiency: Familiarity with data analysis techniques and tools is advantageous but not mandatory. If you're new to data analysis, consider completing introductory courses or tutorials to familiarize yourself with concepts like data manipulation and visualization.
- Curiosity and Eagerness to Learn: An open mindset and willingness to explore new concepts and technologies are crucial prerequisites. Machine learning is a dynamic field with continuous advancements, so a curious attitude and eagerness to learn are essential for success in this course.
- Access to R Studio: It's recommended to have access to R Studio, an integrated development environment (IDE) for R programming. You can download and install R Studio for free from the official website.
- Time and Commitment: Dedication and commitment to completing the course materials and assignments are essential prerequisites. Set aside sufficient time for learning and practice to maximize your understanding and proficiency in R programming and machine learning concepts.
Target Audience
- Aspiring Data Scientists: Individuals aspiring to pursue a career in data science or machine learning, seeking comprehensive training in R programming and advanced analytics techniques.
- Students and Academics: Students studying statistics, computer science, mathematics, or related fields, looking to enhance their skills in data analysis and machine learning using R.
- Software Developers: Developers interested in expanding their expertise to include data science and machine learning, leveraging R programming for data analysis and predictive modeling tasks.
- Business Professionals: Professionals working in business, finance, marketing, or any domain requiring data-driven decision-making, aiming to acquire skills in R programming and machine learning for analytics and insights generation.
- Finance and Investment Professionals: Individuals working in finance, investment, or banking sectors, seeking to leverage R programming for financial analysis, risk modeling, and portfolio optimization.
- Researchers and Academics: Researchers and academics interested in using R programming for statistical analysis, data visualization, and research in various fields such as social sciences, healthcare, and environmental science.
- Entrepreneurs and Startups: Entrepreneurs and startup founders looking to leverage data science and machine learning techniques to gain insights, drive innovation, and make informed business decisions.
- Professionals Seeking Career Transition: Professionals from diverse backgrounds looking to transition into roles in data science, machine learning, or analytics, seeking to acquire relevant skills and knowledge in R programming.
Course Ratings
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This tutorial was really helpful in understanding forecasting using R. The explanation was really easy to understand and the examples were really useful. The coverage of topics was good starting with the basics then going deep into the topics. they have covered simple forecasting methods, transformations, and adjustments, time series regressions and arima models
SHUSHANTH T
Thanks for the course is designed with good quality
Emad Kamel Sadek
It was really a good course to learn the fundamentals in R studio. It will be very useful for the people who wish to learn R for Data Science, Statistics, if they follow this course.
Liyanage Don Kishan Malinda
The course covers a wide range of areas into linear and multiple linear regression and decision tress. It also covers both theory and application using R neural network , time series analysis and gradient boosting machines. I have enjoyed learning in this course very much and found it useful in my work.
Tsui Man Kit