Clustering & Classification With Machine Learning In R

Unlock the Power of Data Science with R: Supervised & Unsupervised Learning

This comprehensive course is your complete guide to mastering both supervised learning and unsupervised learning using R. Covering all the essential aspects of practical data science, this course eliminates the need for multiple courses or expensive books. In today's big data landscape, companies worldwide leverage R to analyze vast amounts of information. Become proficient in unsupervised learning and supervised learning in R to gain a competitive advantage and accelerate your career.

Learn from an Expert Data Scientist:

Minerva Singh, an Oxford University MPhil (Geography and Environment) graduate and Cambridge University PhD holder, brings over 5 years of experience analyzing real-world data using data science techniques. Her work has resulted in publications in international, peer-reviewed journals. Recognizing gaps in existing R data science education, Minerva developed this course to provide a robust and comprehensive understanding of machine learning. Unlike other R instructors, she delves deeply into the machine learning features of R, offering a unique foundation in data science. You'll progress from data reading and cleaning to implementing powerful machine learning algorithms and evaluating their performance in R.

8 Sections Covering Every Aspect of R Machine Learning:

• R Framework Introduction: A thorough introduction to the R environment for data science.
• Data Handling: Learn data structures and how to read in R using various formats like CSV, Excel, and HTML.
• Data Preprocessing: Master data cleaning techniques, including handling missing values (NAs) and data visualization.
• Machine Learning Fundamentals: Explore Machine Learning, Supervised Learning, and Unsupervised Learning in R.
• Model Building & Selection: Learn effective model building and selection strategies.
• ...and much more!

No Prior Experience Needed:

This course is designed for beginners. You'll start with essential R data science basics and techniques, utilizing easy-to-understand, hands-on methods. We'll address even the most challenging concepts in R using real-world data from diverse sources. Unlike courses using synthetic data, this course empowers you to apply R-based data science to real-world problems. You'll gain proficiency in using popular data science packages like caret to work with real data in R, understanding concepts like unsupervised learning, dimension reduction, and supervised learning. All code and data used in the course are provided.

Join the course now and gain the keys to the R Machine Learning kingdom!