Unsupervised learning is a machine learning technique where you do not need to monitor the model.
Instead, should allow the model to work on its own to discover information. Deals primarily with unlabeled data.
Unsupervised learning algorithms allow you to perform more complex processing tasks compared to supervised learning.
Nevertheless, unsupervised learning can be more unpredictable compared to other deep learning methods and natural learning reinforcement.
Why supervised learning?
- Supervised learning allows you to collect data or produce data output from previous experience.
- Helps optimize performance criteria using experience
- Supervised machine learning helps solve various types of real-world computing problems.
K-means for cluster analysis and unsupervised learning in R
The powerful K-means clustering algorithm for cluster analysis and unsupervised machine learning in R
Clustering is a very important part of machine learning. Especially unsupervised machine learning is an emerging topic in the entire field of artificial intelligence.
If we want to learn about cluster analysis, there is no better method to start with than the k-means algorithm.
Who is this course for?
- The course is ideal for professionals who need to use cluster analysis, unsupervised machine learning and R in your field.
- Everyone who wants to learn data science applications in the R environment & R Studio
- All who wish to learn the theory and implementation of unsupervised learning on real world data
- Computer availability and internet
- R programming skills are NOT a requirement, but they would be an advantage
What you will learn
- Understand unsupervised clustering and learning using the R programming language
- Covers both the theoretical background of K-means cluster analysis as well as practical examples in R and R-Studio
- Fully understand the basics of machine learning, cluster analysis and unsupervised machine learning.
- How the K-Means algorithm is defined mathematically and how it is derived.
- Implement K-Means very fast with R encoding: real data examples will be provided
- How the K-Means algorithm works in general. Get an intuitive explanation with easy-to-understand graphics
- Different types of K-med. Fuzzy K-means, K-weighted means and display of K-means results in R
- Evaluate model performance and learn best practices for evaluating machine learning model accuracy
- Implementing the K-Means algorithm in R from scratch. Get a really deep understanding of the working principle
- Learn R programming from scratch: A crash course in R is included so you can get started programming R for machine learning
This course is free thanks to a coupon that you can find below.
Take into account that these types of coupons last for a very short time.
If the coupon has already expired you can purchase the course with a great discount.
The estimated coupon end date is for the day 31 of March – 1 of April, but it can beat at any time.
To obtain the course with your coupon, click on the following button:
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