course-img

Learn Machine Learning with R

$120 $50
Take This Course

Overview:

Welcome to "Learn Machine Learning with R"! This comprehensive course is your essential resource for mastering machine learning techniques using the R programming language. With the increasing demand for data-driven insights, machine learning has become a crucial skill for data scientists and analysts. In this course, you'll explore the fundamentals of machine learning algorithms and their implementation in R, empowering you to leverage data to make informed decisions and predictions.
  • Interactive video lectures by industry experts
  • Instant e-certificate and hard copy dispatch by next working day
  • Fully online, interactive course with Professional voice-over
  • Developed by qualified first aid professionals
  • Self paced learning and laptop, tablet, smartphone friendly
  • 24/7 Learning Assistance
  • Discounts on bulk purchases

Main Course Features:

  • Comprehensive coverage of machine learning algorithms and concepts
  • Hands-on projects and exercises to reinforce learning
  • Implementation of regression, classification, and clustering algorithms in R
  • Exploration of advanced techniques like ensemble learning and dimensionality reduction
  • Guidance on data preprocessing, feature engineering, and model evaluation
  • Best practices for model tuning and optimization in R
  • Real-world case studies and examples to illustrate machine learning applications
  • Access to a supportive online community for collaboration and assistance

Who Should Take This Course:

  • Data scientists, analysts, and professionals looking to enhance their machine learning skills with R
  • Students and researchers interested in exploring machine learning techniques with R
  • Anyone seeking to leverage data for making predictions and gaining insights

Learning Outcomes:

  • Master machine learning algorithms and techniques using R
  • Implement regression, classification, and clustering models for data analysis and prediction
  • Perform data preprocessing, feature engineering, and model evaluation in R
  • Explore advanced machine learning concepts like ensemble learning and dimensionality reduction
  • Optimize model performance through tuning and hyperparameter optimization
  • Apply machine learning techniques to real-world datasets for practical insights
  • Debug and troubleshoot machine learning models effectively in R
  • Stay updated with the latest advancements and trends in machine learning with R.

Certification

Once you’ve successfully completed your course, you will immediately be sent a digital certificate. Also, you can have your printed certificate delivered by post (shipping cost £3.99). All of our courses are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field. Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.

Assessment

At the end of the Course, there will be an online assessment, which you will need to pass to complete the course. Answers are marked instantly and automatically, allowing you to know straight away whether you have passed. If you haven’t, there’s no limit on the number of times you can take the final exam. All this is included in the one-time fee you paid for the course itself.

We guarantee that all our online courses will meet or exceed your expectations. If you are not fully satisfied with a course - for any reason at all - simply request a full refund. We guarantee no hassles. That's our promise to you.

Go ahead and order with confidence!

money_back

Easy to Access
Let's Navigate Together

Course Curriculum

Section 01: Linear Regression and Logistic Regression
Working on Linear Regression
Equation
Making the Regression of the Algorithm
Basic Types of Algorithms
predicting the Salary of the Employee
Making of Simple Linear Regression Model
Plotting Training Set and Work
Section 02: Understanding Dataset
Multiple Linear Regression
Dummy Variable Concept
Predictions Over Year
Difference Between Reference Elimination
Working of the Model
Working on Another Dataset
Backward Elimination Approach
Making of the Model with Full and Null